A data analytics platform may be configured to construct an inferential model for a multivariate observation vector using inferential modeling in combination with component analysis, which may enable the data analytics platform to evaluate only a subset of the variables in the observation vector and then output a predicted version of the multivariate observation vector that includes predicted values for the full set of variables that was originally included in the observation vector. In turn, the data analytics platform may use the predicted version of the multivariate observation vector output by the inferential model to determine whether an anomaly has occurred.
Disclosed herein are systems, computer-readable media, and methods related to modeling on multivariate time series data overlaid with event data. In particular, some examples involve selecting one or more historical time series data arrays similar to a recent time series data array and filtering the similar historical time series data arrays based on event data. Some examples can also involve training a localized temporal forecasting model using the filtered historical time series data arrays. Some examples can include building and/or training the localized temporal forecasting model at or near a time that a forecast is needed.
Computing systems and methods for determining aggregated risk are disclosed herein. An exemplary computing platform includes: a communication interface, one or more processors, a non-transitory computer-readable medium, and program instructions stored on the non-transitory computer-readable medium. The program instructions, when executed, cause the computing platform to: execute a set of predictive models that are each configured to (i) evaluate operating data for the asset and (ii) output a respective prediction related to an operation of the asset; detect a triggering event for determining an aggregated risk value for the asset; identify (i) a set of predictions related to the operation of the asset and (ii) a risk dataset; determine the aggregated risk value of the asset based on the set of predictions and the risk dataset; and cause a client device to display a visual representation of the aggregated risk value.
A computing platform is configured to: (a) generate a predicted health distribution of an asset for a failure mode based on a prior health distribution and a wear rate distribution. The computing platform is further configured to (b) update, the predicted health distribution based on (i) an observed state distribution corresponding to an observed state associated with the asset and (ii) a normalized value representative of a probability of the observed state over one or more health values of the asset. The computing platform is further configured to (c) generate a survival curve of the asset based on the predicted health distribution and a set of wear rates; iteratively perform (a)-(c) for each failure mode of the set of failure modes; aggregate the survival curve for each failure mode into an aggregate survival curve; and cause a client device to display a visual representation of the aggregate survival curve.
Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model's input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.
G05B 13/04 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
A data analytics platform may be configured to construct an inferential model for a multivariate observation vector using inferential modeling in combination with component analysis, which may enable the data analytics platform to evaluate only a subset of the variables in the observation vector and then output a predicted version of the multivariate observation vector that includes predicted values for the full set of variables that was originally included in the observation vector. In turn, the data analytics platform may use the predicted version of the multivariate observation vector output by the inferential model to determine whether an anomaly has occurred.
Disclosed herein are systems, computer-readable media, and methods related to modeling on multivariate time series data overlaid with event data. In particular, some examples involve selecting one or more historical time series data arrays similar to a recent time series data array and filtering the similar historical time series data arrays based on event data. Some examples can also involve training a localized temporal forecasting model using the filtered historical time series data arrays. Some examples can include building and/or training the localized temporal forecasting model at or near a time that a forecast is needed.
Disclosed herein is a data-driven approach for determining and presenting a more intelligent measure of the probability of failure of a substation. The disclosed approach generally involves (i) deriving respective failure probabilities of the individual assets within the substation by taking into consideration certain operating, environmental, maintenance or other types of data related to the individual assets, (ii) determining the electrical configuration of the substation, (iii) determining a substation failure probability based on the respective failure probabilities of the individual assets within the substation and the electrical configuration of the substation, and then (iv) presenting the probability of substation failure and/or the respective failure probabilities for the individual assets to a user in various ways. A user may use this probability of failure together with knowledge of the impact or consequence of a failure at the substation to make planning decisions for the substation or the electrical system.
H02B 13/025 - Dispositions pour la sécurité, p. ex. en cas de surpression ou d'incendie causés par un défaut électrique
G01R 31/327 - Tests d'interrupteurs de circuit, d'interrupteurs ou de disjoncteurs
H02H 7/22 - Circuits de protection de sécurité spécialement adaptés aux machines ou aux appareils électriques de types particuliers ou pour la protection sectionnelle de systèmes de câble ou de ligne, et effectuant une commutation automatique dans le cas d'un changement indésirable des conditions normales de travail pour appareillage de distribution, p. ex. système de barre omnibusCircuits de protection de sécurité spécialement adaptés aux machines ou aux appareils électriques de types particuliers ou pour la protection sectionnelle de systèmes de câble ou de ligne, et effectuant une commutation automatique dans le cas d'un changement indésirable des conditions normales de travail pour dispositifs de commutation
Disclosed herein is a data-driven approach for determining and presenting a more intelligent measure of the probability of failure of a substation. The disclosed approach generally involves (i) deriving respective failure probabilities of the individual assets within the substation by taking into consideration certain operating, environmental, maintenance or other types of data related to the individual assets, (ii) determining the electrical configuration of the substation, (iii) determining a substation failure probability based on the respective failure probabilities of the individual assets within the substation and the electrical configuration of the substation, and then (iv) presenting the probability of substation failure and/or the respective failure probabilities for the individual assets to a user in various ways. A user may use this probability of failure together with knowledge of the impact or consequence of a failure at the substation to make planning decisions for the substation or the electrical system.
Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve determining health metrics that estimate the operating health of an asset or a part thereof, determining recommended operating modes for assets, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G01M 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
G05B 19/18 - Commande numérique [CN], c.-à-d. machines fonctionnant automatiquement, en particulier machines-outils, p. ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'un programme sous forme numérique
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
G01D 3/08 - Dispositions pour la mesure prévues pour les objets particuliers indiqués dans les sous-groupes du présent groupe avec dispositions pour protéger l'appareil, p. ex. contre les fonctionnements anormaux, contre les pannes
G06F 11/20 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel en utilisant un masquage actif du défaut, p. ex. en déconnectant les éléments défaillants ou en insérant des éléments de rechange
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques
H04L 12/707 - Prévention ou récupération du défaut de routage, p.ex. reroutage, redondance de route "virtual router redundancy protocol" [VRRP] ou "hot standby router protocol" [HSRP] par redondance des chemins d’accès
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
11.
Computer system and method for presenting asset insights at a graphical user interface
A computing system is configured to derive insights related to asset operation and present these insights via a GUI. To these ends, the computing system (a) receives data related to the operation of assets, (b) based on this data, derives a plurality of insights related to the operation of at least a subset of the assets, (c) from the insights, defines a given subset of insights to be presented to a user, (d) defines at least one aggregated insight representative of one or more individual insights in the given subset of insights that are related to a common underlying problem, and (e) causes the user's client station to display a visualization of the given subset of insights including (i) an insights pane that provides a high-level overview of the subset of insights and (ii) a details pane that provides additional details regarding a selected one of the subset of insights.
G06F 11/00 - Détection d'erreursCorrection d'erreursContrôle de fonctionnement
H04L 67/125 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance en impliquant la commande des applications des terminaux par un réseau
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Data mining; data automation and collection services using proprietary software to evaluate, analyze and collect service data; electronic monitoring and reporting of physical properties of industrial assets using computers and sensors; data mining, namely, predictive analytics and data science services for providing predictions and recommendations related to the operation, repair, or maintenance of industrial assets; data mining, namely, predictive analytics and data science services for industrial asset management and optimization; data mining, namely, predictive analytics and data science services in the field of operational technology; providing on-line non-downloadable software for use in connection with data mining; providing on-line non-downloadable software for use in connection with data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in connection with electronic monitoring and reporting of physical properties of industrial assets using computers or sensors; providing on-line non-downloadable software for use in connection with predictive analytics and data science services for providing predictions and recommendations related to the operation, repair, or maintenance of industrial assets; providing on-line non-downloadable software for use in connection with predictive analytics and data science services for industrial asset management and optimization; providing on-line non-downloadable software for use in connection with predictive analytics and data science services in the field of operational technology; providing on-line non-downloadable software for use in predictive analytics and data science services; providing on-line non-downloadable software for use in industrial analytics for predicting and preventing failures in industrial assets or operations; providing on-line non-downloadable software for use in industrial modeling; providing on-line non-downloadable software for use in monitoring, repairing, or maintaining industrial assets; providing on-line non-downloadable software for use in preventing failures in industrial assets by monitoring industrial assets such that they may be proactively repaired or maintained; providing on-line non-downloadable software for monitoring the condition of industrial assets; providing on-line non-downloadable software for accessing data related to industrial assets; providing on-line non-downloadable software for analyzing data related to industrial assets
Disclosed herein is a data science platform that is built with a specific focus on monitoring and analyzing the operation of industrial assets, such as trucking assets, rail assets, construction assets, mining assets, wind assets, thermal assets, oil-and-gas assets, and manufacturing assets, among other possibilities. The disclosed data science platform is configured to carry out operations including (i) ingesting asset-related data from various different data sources and storing it for downstream use, (ii) transforming the ingested asset-related data into a desired formatting structure and then storing it for downstream use, (iii) evaluating the asset-related data to derive insights about an asset's operation that may be of interest to a platform user, which may involve data science models that have been specifically designed to analyze asset-related data in order to gain a deeper understanding of an asset's operation, and (iv) presenting derived insights and other asset-related data to platform users.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
In examples, a computing system is configured to detect rotor imbalance at wind turbines by (1) obtaining sets of historical-vibration data for turbines, each set comprising vibration data captured by a given turbine's multi-dimensional sensor, (2) deriving a rotor-imbalance-detection model by: (a) for each turbine, (i) transforming time segments of the turbine's historical-vibration dataset into a frequency-domain representation, and (ii) for each time segment, using the frequency-domain representation for the time segment to derive a set of harmonic-mode values for at least one frequency-range of interest, thereby deriving a time-series set of harmonic-mode values for the turbine, and (b) performing an evaluation of the time-series sets for the turbines, thereby deriving the rotor-imbalance-detection model, (3) based on received vibration data for a given turbine from a reference time, executing the derived model, thereby detecting a rotor imbalance at the given turbine, and (4) transmitting a notification of the rotor imbalance.
F03D 7/02 - Commande des mécanismes moteurs à vent les mécanismes moteurs à vent ayant l'axe de rotation sensiblement parallèle au flux d'air pénétrant dans le rotor
Disclosed herein is a data science platform that is built with a specific focus on monitoring and analyzing the operation of industrial assets, such as trucking assets, rail assets, construction assets, mining assets, wind assets, thermal assets, oil-and-gas assets, and manufacturing assets, among other possibilities. The disclosed data science platform is configured to carry out operations including (i) ingesting asset-related data from various different data sources and storing it for downstream use, (ii) transforming the ingested asset-related data into a desired formatting structure and then storing it for downstream use, (iii) evaluating the asset-related data to derive insights about an asset's operation that may be of interest to a platform user, which may involve data science models that have been specifically designed to analyze asset-related data in order to gain a deeper understanding of an asset's operation, and (iv) presenting derived insights and other asset-related data to platform users.
A computing system is configured to detect irregular yawing at wind turbines. To this end, the computing system (i) for each respective turbine of an identified cluster of wind turbines: (a) obtains yaw-activity data indicative of the respective turbine's yaw activity during a window of time, and (b) based on obtained yaw-activity data, derives a yaw-activity-measure dataset having measures of the respective turbine's yaw activity during time intervals within the window of time, (ii) based on the respective yaw-activity-measure datasets for the turbines in the cluster, derives a cluster-level yaw-activity-measure dataset, (iii) evaluates the respective yaw-activity-measure dataset for one or more turbines in the cluster as compared to the cluster-level yaw-activity-measure dataset, (iv) based on the evaluation, identifies at least one turbine of the cluster that exhibited irregular yaw activity, and (v) transmits, to an output device, a notification of the irregular yaw activity at the at least one turbine.
F03D 7/02 - Commande des mécanismes moteurs à vent les mécanismes moteurs à vent ayant l'axe de rotation sensiblement parallèle au flux d'air pénétrant dans le rotor
G01W 1/02 - Instruments pour indiquer des conditions atmosphériques par mesure de plusieurs variables, p. ex. humidité, pression, température, nébulosité ou vitesse du vent
F03D 17/00 - Surveillance ou test de mécanismes moteurs à vent, p. ex. diagnostics
17.
Computer system and method for determining an orientation of a wind turbine nacelle
A platform may obtain reference data that is indicative of an expected orientation of a wind turbine at one or more past times and use the reference data to determine the expected orientation of the wind turbine at each such times. In addition, the platform may obtain measurement data that is indicative of a measured orientation of the wind turbine at each of the one or more past times and use the measurement data to determine the measured orientation of the wind turbine at each such time. Thereafter, the platform may determine an orientation offset for the wind turbine based on a comparison between the expected and measured orientation of the wind turbine at each of the one or more past times and then cause the orientation offset to be applied to at least one nacelle orientation reported by the wind turbine.
F03D 7/02 - Commande des mécanismes moteurs à vent les mécanismes moteurs à vent ayant l'axe de rotation sensiblement parallèle au flux d'air pénétrant dans le rotor
Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve determining health metrics that estimate the operating health of an asset or a part thereof, determining recommended operating modes for assets, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G05B 19/18 - Commande numérique [CN], c.-à-d. machines fonctionnant automatiquement, en particulier machines-outils, p. ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'un programme sous forme numérique
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
G01D 3/08 - Dispositions pour la mesure prévues pour les objets particuliers indiqués dans les sous-groupes du présent groupe avec dispositions pour protéger l'appareil, p. ex. contre les fonctionnements anormaux, contre les pannes
G06F 11/20 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel en utilisant un masquage actif du défaut, p. ex. en déconnectant les éléments défaillants ou en insérant des éléments de rechange
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques
H04L 12/707 - Prévention ou récupération du défaut de routage, p.ex. reroutage, redondance de route "virtual router redundancy protocol" [VRRP] ou "hot standby router protocol" [HSRP] par redondance des chemins d’accès
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
19.
COMPUTER SYSTEM & METHOD FOR PRESENTING ASSET INSIGHTS AT A GRAPHICAL USER INTERFACE
A computing system is configured to derive insights related to asset operation and present these insights via a GUI. To these ends, the computing system (a) receives data related to the operation of assets, (b) based on this data, derives a plurality of insights related to the operation of at least a subset of the assets, (c) from the insights, defines a given subset of insights to be presented to a user, (d) defines at least one aggregated insight representative of one or more individual insights in the given subset of insights that are related to a common underlying problem, and (e) causes the user's client station to display a visualization of the given subset of insights including (i) an insights pane that provides a high-level overview of the subset of insights and (ii) a details pane that provides additional details regarding a selected one of the subset of insights.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
20.
COMPUTER SYSTEM AND METHOD FOR CREATING AN EVENT PREDICTION MODEL
Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model's input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
21.
Computer system and method for creating an event prediction model
Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model's input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.
G05B 13/04 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
A computing system is configured to derive insights related to asset operation and present these insights via a GUI. To these ends, the computing system (a) receives data related to the operation of assets, (b) based on this data, derives a plurality of insights related to the operation of at least a subset of the assets, (c) from the insights, defines a given subset of insights to be presented to a user, (d) defines at least one aggregated insight representative of one or more individual insights in the given subset of insights that are related to a common underlying problem, and (e) causes the user's client station to display a visualization of the given subset of insights including (i) an insights pane that provides a high-level overview of the subset of insights and (ii) a details pane that provides additional details regarding a selected one of the subset of insights.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services Data mining; data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in connection with data mining; providing on-line non-downloadable software for use in connection with data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for managing work orders related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for optimizing work orders related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for analyzing historical work order data related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for identifying and correcting errors in work orders related the repair or maintenance of industrial assets; providing on-line non-downloadable software for providing recommendations related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for repair or maintenance scheduling for industrial assets; providing on-line non-downloadable software for accessing data related to industrial assets; providing on-line non-downloadable software for analyzing data related to industrial assets
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services; business services, namely, providing predictive analytics and data science services related to repair or maintenance scheduling for industrial assets; business services, namely, providing predictive analytics and data science services related to optimizing utilization of industrial assets; business services, namely, providing predictive analytics and data science services related to increasing production output of industrial assets Data mining; data automation and collection services using proprietary software to evaluate, analyze and collect service data; electronic monitoring and reporting of physical properties of industrial assets using computers and sensors; predictive analytics and data science services for providing predictions and recommendations related to the operation, repair, or maintenance of industrial assets; predictive analytics and data science services for industrial asset management and optimization; predictive analytics and data science services in the field of operational technology; providing on-line non-downloadable software for use in connection with data mining; providing on-line non-downloadable software for use in connection with data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in connection with electronic monitoring and reporting of physical properties of industrial assets using computers or sensors; providing on-line non-downloadable software for use in connection with predictive analytics and data science services for providing predictions and recommendations related to the operation, repair, or maintenance of industrial assets; providing on-line non-downloadable software for use in connection with predictive analytics and data science services for industrial asset management and optimization; providing on-line non-downloadable software for use in connection with predictive analytics and data science services in the field of operational technology; providing on-line non-downloadable software for use in predictive analytics and data science services; providing on-line non-downloadable software for use in industrial analytics for predicting and preventing failures in industrial assets or operations; providing on-line non-downloadable software for use in industrial modeling; providing on-line non-downloadable software for use in monitoring, repairing, or maintaining industrial assets; providing on-line non-downloadable software for use in preventing failures in industrial assets by monitoring industrial assets such that they may be proactively repaired or maintained
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services; business services, namely, providing predictive analytics and data science services related to repair or maintenance scheduling for industrial assets; business services, namely, providing predictive analytics and data science services related to optimizing utilization of industrial assets; business services, namely, providing predictive analytics and data science services related to increasing production output of industrial assets Data mining; data automation and collection services using proprietary software to evaluate, analyze and collect service data; electronic monitoring and reporting of physical properties of industrial assets using computers and sensors; predictive analytics and data science services for providing predictions and recommendations related to the operation, repair, or maintenance of industrial assets; predictive analytics and data science services for industrial asset management and optimization; predictive analytics and data science services in the field of operational technology; providing on-line non-downloadable software for use in connection with data mining; providing on-line non-downloadable software for use in connection with data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in connection with electronic monitoring and reporting of physical properties of industrial assets using computers or sensors; providing on-line non-downloadable software for use in connection with predictive analytics and data science services for providing predictions and recommendations related to the operation, repair, or maintenance of industrial assets; providing on-line non-downloadable software for use in connection with predictive analytics and data science services for industrial asset management and optimization; providing on-line non-downloadable software for use in connection with predictive analytics and data science services in the field of operational technology; providing on-line non-downloadable software for use in predictive analytics and data science services; providing on-line non-downloadable software for use in industrial analytics for predicting and preventing failures in industrial assets or operations; providing on-line non-downloadable software for use in industrial modeling; providing on-line non-downloadable software for use in monitoring, repairing, or maintaining industrial assets; providing on-line non-downloadable software for use in preventing failures in industrial assets by monitoring industrial assets such that they may be proactively repaired or maintained
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services Data mining; data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in connection with data mining; providing on-line non-downloadable software for use in connection with data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for managing work orders related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for optimizing work orders related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for analyzing historical work order data related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for identifying and correcting errors in work orders related the repair or maintenance of industrial assets; providing on-line non-downloadable software for providing recommendations related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for repair or maintenance scheduling for industrial assets; providing on-line non-downloadable software for accessing data related to industrial assets; providing on-line non-downloadable software for analyzing data related to industrial assets
27.
Systems and methods for detecting and remedying software anomalies
A computing platform may obtain observed data vectors related to the operation of a topology of nodes that represents a software application running on an uncontrolled platform, wherein each observed data vector comprises data values captured for a given set of operating variables at a particular point in time. After obtaining the observed data vectors, the computing platform may apply an anomaly detection model to the observed data vectors and then based on the anomaly detection model, may identify an anomaly in at least one operating variable. In turn, the computing platform may determine whether each identified anomaly is indicative of a problem related to the application, and based on a determination that an identified anomaly is indicative of a problem related to the software application, cause a client station to present a notification.
G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
A method for multi-tenant authorization includes receiving, from a user account of a multi-tenant computer system, a request for a resource of the multi-tenant computer system. The method further includes determining whether the resource corresponds to a local resource that is local to the user account or to a nonlocal resource that is not local to the user account. The method further includes identifying, by a processing device, a local access control policy of the user account, corresponding to the local resource, or a visiting access control policy of the user account, corresponding to the nonlocal resource. The method further includes determining that the identified access control policy of the user account comprises an access permission corresponding to the resource. The method further includes controlling access to the resource of the multi-tenant computer system based on the access permission.
A computing system may create an anomaly detection model to detect anomalies in multivariate data originating from a given data source by extracting a model object for the anomaly detection model using a first set of training data originating from the given data source, establishing starting values of a set of anomaly thresholds for the anomaly detection model using the extracted model object and a second set of training data originating from the given data source, and refining the starting values of the set of anomaly thresholds for at least a subset of the variables included in the multivariate data using the extracted model object and a set of test data. In turn, the computing system may use the anomaly detection model to monitor for anomalies in observation data originating from the given data source.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
A method for multi-tenant authorization includes receiving, from a user account of a multi-tenant computer system, a request for a resource of the multi-tenant computer system. The method further includes determining whether the resource corresponds to a local resource that is local to the user account or to a nonlocal resource that is not local to the user account. The method further includes identifying, by a processing device, a local access control policy of the user account, corresponding to the local resource, or a visiting access control policy of the user account, corresponding to the nonlocal resource. The method further includes determining that the identified access control policy of the user account comprises an access permission corresponding to the resource. The method further includes controlling access to the resource of the multi-tenant computer system based on the access permission.
When two event prediction models produce different numbers of catches, a computer system may be configured to determine which of the two models has the higher net value based on how a “Break-Even Alert Value Ratio” for the models compares to an estimate of the how many false flags are worth trading for one catch. Further, when comparing two event prediction models, a computer system may be configured to determine “catch equivalents” and “false-flag equivalents” numbers for the two different models based on potential-value and impact scores assigned to the models' predictions, and the computing system then use these “catch equivalents” and “false-flag equivalents” numbers in place of “catch” and “false flag” numbers that may be determined using other approaches.
G05B 13/04 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
32.
COMPUTER SYSTEM AND METHOD FOR RECOMMENDING AN OPERATING MODE OF AN ASSET
Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve determining health metrics that estimate the operating health of an asset or a part thereof, determining recommended operating modes for assets, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.
Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve determining health metrics that estimate the operating health of an asset or a part thereof, determining recommended operating modes for assets, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of operating conditions that normally result in triggering of abnormal-condition indicators, among other examples.
A computing system may operate in a first mode during which it calculates a set of training metrics on a running basis as a stream of multivariate data points originating from a data source is being received. While operating in the first mode, the computing system may determine that the set of training metrics has reached a threshold level of stability. In response, the computing system may transition to a second mode during which its extracts a model object and calculates a set of model parameters for an anomaly detection model. While operating in the second mode, the computing system may determine that the set of model parameters has reached a threshold level of stability. In response, the computing system may transition to a third mode during which it uses the anomaly detection model to monitor for anomalies in the stream of multivariate data points originating from the data source.
The example systems, methods, and devices disclosed herein generally relate to handling operating data from non-communicative assets. In some instances, a data-analytics platform receives operating data points from a given asset of a plurality of assets. Based on that data, the data-analytics platform detects a communication abnormality at the given asset, in accordance with one or more techniques disclosed herein. In response to detecting the communication abnormality, the data-analytics platform designates the given asset as being non-communicative. The data-analytics platform handles operating data points received from the given asset in accordance with the non-communicative designation.
A computing system may be configured to obtain operating data for a manufacturing network that comprises a plurality of edge nodes, a plurality of intermediate nodes, and a root node. Based on the operating data, the computing system may determine a respective critical state indicator for each node in at least a given segment of the manufacturing network. Based on the respective critical state indicator for each node in the given segment, the computing system may recursively determine a respective health score for each node in the given segment of the manufacturing network. Based on the respective health score for each node in the given segment of the manufacturing network, the computing system may identify one or more nodes in the given segment that are anomalous and cause a client station to present a report of the one or more nodes that are identified to be anomalous.
Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to updating a routing table. In particular, examples involve based on a predictive model, determining that a given asset of a plurality of assets in a mesh network is likely to be unavailable within a given period of time in the future and in response to the determining, causing a routing configuration for at least one other asset in the mesh network to be updated.
G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
G06F 11/00 - Détection d'erreursCorrection d'erreursContrôle de fonctionnement
G06F 11/263 - Génération de signaux d'entrée de test, p. ex. vecteurs, formes ou séquences de test
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
A computing system may provide an interface for creating a data processing pipeline through which the computing system may receive configuration information for a given pipeline that is configured to receive streaming messages from a given data source, process each of the streaming messages, and then output a processed version of at least a subset of the streaming messages to a given data sink. The given pipeline may comprise a chain of two or more operators, which may take the form of enrichers, routers, and/or transformers. The computing system may then use the received configuration information to create the given pipeline (e.g., an enrichment pipeline comprising at least two enrichers). In turn, the computing system may deploy the given pipeline for use in processing streaming messages received from the given data source.
A computing system may provide an interface for creating a data processing pipeline through which the computing system may receive configuration information for a given pipeline that is configured to receive streaming messages from a given data source, process each of the streaming messages, and then output a processed version of at least a subset of the streaming messages to a given data sink. The given pipeline may comprise a chain of two or more operators, which may take the form of enrichers, routers, and/or transformers. The computing system may then use the received configuration information to create the given pipeline. In turn, the computing system may deploy the given pipeline for use in processing streaming messages received from the given data source.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; business data analysis services; data processing services. Data mining; data automation and collection services using proprietary software to evaluate, analyze and collect service data; predictive analytics and data science services for providing predictions and recommendations related to the operation, repair, and maintenance of industrial machinery; predictive analytics and data science services for industrial machinery management and optimization; predictive analytics and data science services in the field of operational technology; electronic monitoring and reporting of physical properties of industrial machinery; providing on-line non-downloadable software for use in connection with any one or more of the previously named services; providing on-line non-downloadable software for use in predictive analytics and data science services; providing on-line non-downloadable software for use in industrial analytics; providing on-line non-downloadable software for use in industrial modeling; providing on-line non-downloadable software for use in monitoring, repairing, and maintaining industrial machinery.
41.
COORDINATING EXECUTION OF PREDICTIVE MODELS BETWEEN MULTIPLE DATA ANALYTICS PLATFORMS TO PREDICT PROBLEMS AT AN ASSET
To distribute execution of a predictive model between multiple data analytics platforms, a first platform may be provisioned with a set of precursor detection models and a second platform may be provisioned with a set of precursor analysis models. Based on a given precursor detection model, the first platform may detect an occurrence of a given type of precursor event at a given asset and send data associated with the occurrence to the second platform. In response, the second platform may (a) identify at least one precursor analysis model that is associated with the given type of precursor event and predicts whether a given type of problem is present at an asset and (b) execute the at least one precursor analysis model to perform a deeper analysis of the occurrence and thereby output a prediction of whether the given type of problem is present at the given asset.
A method may include receiving, from a client device, a request for a resource of a computer system, determining one or more roles of a user associated with the client device, and determining one or more attributes of the user. The method may include determining one or more attributes of the resource and determining an access permission based on the one or more roles of the user and the resource. The method may include generating, by a processing device, a modified access permission by modifying the access permission based on at least one of: the one or more attributes of the user or the one or more attributes of the resource and providing or denying access to the resource of the computer system based on the modified access permission.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
A method may include receiving, from a client device, a request for a resource of a computer system, determining one or more roles of a user associated with the client device, and determining one or more attributes of the user. The method may include determining one or more attributes of the resource and determining an access permission based on the one or more roles of the user and the resource. The method may include generating, by a processing device, a modified access permission by modifying the access permission based on at least one of: the one or more attributes of the user or the one or more attributes of the resource and providing or denying access to the resource of the computer system based on the modified access permission.
A method may include receiving a request for a resource of a computer system. The method may include determining one or more attributes of a user associated with the request, wherein the one or more attributes are based on a status of the user in an organization hierarchy, the organization hierarchy comprising one or more sub organizations corresponding to the user. The method may include determining that the request comprises one or more attribute names. The method may include: in response to receiving the request, generating, by a processing device, an access permission based on the organization hierarchy corresponding to the user and the one or more attribute names, by replacing the one or more attribute names with the one or more attributes. The method may include providing or denying access to the resource of the computer system based on the access permission.
The example systems, methods, and devices disclosed herein generally relate to generating create a supervised failure model for assets in the given fleet that is configured to receive operating data as inputs and output a prediction as to the occurrence of a given failure type at the asset. In some instances, a data analytics platform may create and use an unsupervised failure model for a subset of the assets, use the respective unsupervised failure models to detect a set of anomalies that are each suggestive of a prior failure occurrence, from the set of anomalies, identify a subset of anomalies that are each suggest of a prior failure occurrence of the given failure type, and create the supervised failure model using failure data for the identified subset of anomalies.
The example systems, methods, and devices disclosed herein generally relate to generating create a supervised failure model for assets in the given fleet that is configured to receive operating data as inputs and output a prediction as to the occurrence of a given failure type at the asset. In some instances, a data analytics platform may create and use an unsupervised failure model for a subset of the assets, use the respective unsupervised failure models to detect a set of anomalies that are each suggestive of a prior failure occurrence, from the set of anomalies, identify a subset of anomalies that are each suggest of a prior failure occurrence of the given failure type, and create the supervised failure model using failure data for the identified subset of anomalies.
A computing system may evaluate the operation of a manufacturing network using at least two of (a) macro-level threshold criteria indicating anomalous operation of the manufacturing network as a whole, (b) micro-level threshold criteria indicating anomalous operation of any of a plurality of micro-networks in the manufacturing network, (c) path-level threshold criteria indicating anomalous operation of any of a plurality of node paths in the manufacturing network, or (d) node-level threshold criteria indicating anomalous operation of any of a plurality of individual nodes in the manufacturing network. Based on the evaluating, computing system may identify at least one anomaly in the manufacturing network and then trigger at least one action that is directed to resolving the at least one anomaly.
Computing systems, devices, and methods for performing a virtual load test are disclosed herein. In accordance with the present disclosure, an asset data platform may define a respective range of acceptable values for each load-test variable in a set of load-test variables. The asset data platform may then receive one or more under-load reports from a given asset, and carry out a virtual load test for the given asset by, performing a comparison between the respective observation value for the load-test variable included in the most recent under-load report and the respective range of acceptable values for the load-test variable. In turn, the asset data platform may identify load-test variables for which the respective observation value falls outside of the respective range of acceptable values, and may then cause a client station to present results of the virtual load test for the given asset.
Computing systems, devices, and methods for performing a virtual load test are disclosed herein. In accordance with the present disclosure, an asset data platform may define a respective range of acceptable values for each load-test variable in a set of load-test variables. The asset data platform may then receive one or more under-load reports from a given asset, and carry out a virtual load test for the given asset by, performing a comparison between the respective observation value for the load-test variable included in the most recent under-load report and the respective range of acceptable values for the load-test variable. In turn, the asset data platform may identify load-test variables for which the respective observation value falls outside of the respective range of acceptable values, and may then cause a client station to present results of the virtual load test for the given asset.
G06F 11/00 - Détection d'erreursCorrection d'erreursContrôle de fonctionnement
G06F 11/22 - Détection ou localisation du matériel d'ordinateur défectueux en effectuant des tests pendant les opérations d'attente ou pendant les temps morts, p. ex. essais de mise en route
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
50.
Computer system and method for detecting anomalies in multivariate data
A data analytics platform may be configured to construct an inferential model for a multivariate observation vector using inferential modeling in combination with component analysis, which may enable the data analytics platform to evaluate only a subset of the variables in the observation vector and then output a predicted version of the multivariate observation vector that includes predicted values for the full set of variables that was originally included in the observation vector. In turn, the data analytics platform may use the predicted version of the multivariate observation vector output by the inferential model to determine whether an anomaly has occurred.
A data analytics platform may be configured to construct an inferential model for a multivariate observation vector using inferential modeling in combination with component analysis, which may enable the data analytics platform evaluate only a subset of the variables in the observation vector and then output a predicted version of the multivariate observation vector that includes predicted values for the full set of variables that was originally included in the observation vector. In turn, the data analytics platform may use the predicted version of the multivariate observation vector output by the inferential model to determine whether an anomaly has occurred.
Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and using a predictive model that is configured to output an indication of whether at least one failure type from the group of possible failure types is likely to occur at an asset within the given period of time in the future.
G01D 3/08 - Dispositions pour la mesure prévues pour les objets particuliers indiqués dans les sous-groupes du présent groupe avec dispositions pour protéger l'appareil, p. ex. contre les fonctionnements anormaux, contre les pannes
G01M 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
G06F 11/20 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel en utilisant un masquage actif du défaut, p. ex. en déconnectant les éléments défaillants ou en insérant des éléments de rechange
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
G06N 7/00 - Agencements informatiques fondés sur des modèles mathématiques spécifiques
H04L 12/707 - Prévention ou récupération du défaut de routage, p.ex. reroutage, redondance de route "virtual router redundancy protocol" [VRRP] ou "hot standby router protocol" [HSRP] par redondance des chemins d’accès
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
53.
Dynamic execution of predictive models and workflows
Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve defining and deploying aggregate, predictive models and corresponding workflows, defining and deploying individualized, predictive models and/or corresponding workflows, and dynamically adjusting the execution of model-workflow pairs.
G05B 13/04 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
54.
Computer system and method of detecting manufacturing network anomalies
A computing system may be configured to monitor the operation of a plurality of nodes in a manufacturing network that comprises a plurality of edge nodes, a plurality of intermediate nodes, and a root node. While monitoring the operation of the plurality of nodes, the computing system may identify a given time at which at least one node in the manufacturing network satisfies node-level threshold criteria indicating anomalous operation of the node and responsively evaluate the operation of the manufacturing network at the given time using one or more of macro-level threshold, micro-level threshold criteria, path-level threshold criteria, and node-level threshold criteria. Based on the evaluation, the computing system may identify an anomaly in the manufacturing network at the given time and then cause a client station to present an alert indicating the anomaly.
A set of successive images for a given area may comprise a first, second, and third images representing different times. Comparisons may be performed between the first image and the second image, the first image and the third image, and the second image and the third image. The respective outputs of the three pairwise comparisons may be evaluated to identify any sub-areas of the given area where one or more temporal patterns of change have occurred. For instance, any sub-area of the given area that exhibits a change between the first and second images, a change between the second and third images, and an absence of change between the second and third images may be identified as a persistent change. An indication that the temporal pattern of change has occurred at each identified sub-area of the given area may then be output to a client station.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
56.
COMPUTER SYSTEM AND METHOD FOR CLASSIFYING TEMPORAL PATTERNS OF CHANGE IN IMAGES OF AN AREA
A set of successive images for a given area may comprise a first, second, and third images representing different times. Comparisons may be performed between the first image and the second image, the first image and the third image, and the second image and the third image. The respective outputs of the three pairwise comparisons may be evaluated to identify any sub-areas of the given area where one or more temporal patterns of change have occurred. For instance, any sub-area of the given area that exhibits a change between the first and second images, a change between the second and third images, and an absence of change between the second and third images may be identified as a persistent change. An indication that the temporal pattern of change has occurred at each identified sub-area of the given area may then be output to a client station.
Disclosed herein are systems, devices, and methods related to the interaction between a remote analytics system and a plurality of assets. In one aspect, the remote analytics system may be configured to define an approximation of a baseline predictive model that comprises a set of approximation functions and corresponding regions of input data values, which may be referred to as "base regions." In another aspect, the remote analytics system may be configured to "compress" the time-series values captured for a given operating data variable using an approximation of the time-series values that comprises a set of approximation functions and corresponding base regions.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06N 5/04 - Modèles d’inférence ou de raisonnement
58.
Computer system and method for predicting an abnormal event at a wind turbine in a cluster
The example systems, methods, and devices disclosed herein generally relate to performing predictive analytics on behalf of wind turbines. In some instances, a data-analytics platform defines and executes a predictive model for a specific wind turbine. The predictive model may be defined and executed based on operating data for the specific wind turbine and for other wind turbines that experience similar environmental conditions as the specific wind turbine and that are operating in an expected operational state. In response to executing the predictive model, the data-analytics platform may cause an action to occur at the specific wind turbine or cause a user interface to display a representation of the output of the executed model, among other possibilities.
G05B 19/042 - Commande à programme autre que la commande numérique, c.-à-d. dans des automatismes à séquence ou dans des automates à logique utilisant des processeurs numériques
The example systems, methods, and devices disclosed herein generally relate to performing predictive analytics on behalf of wind turbines. In some instances, a data-analytics platform defines and executes a predictive model for a specific wind turbine. The predictive model may be defined and executed based on operating data for the specific wind turbine and for other wind turbines that experience similar environmental conditions as the specific wind turbine and that are operating in an expected operational state. In response to executing the predictive model, the data-analytics platform may cause an action to occur at the specific wind turbine or cause a user interface to display a representation of the output of the executed model, among other possibilities.
G05B 13/04 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
G05B 15/02 - Systèmes commandés par un calculateur électriques
F03D 17/00 - Surveillance ou test de mécanismes moteurs à vent, p. ex. diagnostics
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services. Predictive analytics and data science services in the field of operational technology; electronic monitoring and reporting of physical properties of an industrial asset using computers and sensors; data mining; electric sensor reading and data analysis; electronic sensor reading and data analysis; design and development of integrated data collection and wireless transmission hardware systems for equipment and for software applications associated with that equipment at industrial assets; data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in connection with predictive analytics and data science services in the field of operational technology, monitoring and reporting of physical properties of an industrial asset using computers and sensors, data mining, electric sensor reading and data analysis, electronic sensor reading and data analysis, integrated data collection and wireless transmission hardware systems for equipment, and for software applications associated with that equipment at industrial assets, and data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in predictive analytics and data science services; configuration and customization of computer databases featuring information for use in predictive analytics and data science services; providing on-line non-downloadable software for use in repair or maintenance of industrial assets; configuration and customization of computer databases featuring technical information for use in repair or maintenance of industrial assets; providing on-line non-downloadable software for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning related to industrial assets or operations; providing on-line non-downloadable software for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning in the field of operational technology; configuration and customization of computer databases featuring information for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning related to industrial assets or operations; configuration and customization of computer databases featuring information for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning in the field of operational technology; providing on-line non-downloadable software for use in industrial modeling, namely, for use in computer modeling of industrial assets or operations; configuration and customization of computer databases featuring information for use in industrial modeling, namely, for use in computer modeling of industrial assets or operations.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Integrated data collection and wireless transmission hardware and software systems; telematics apparatuses, namely, wireless communications devices which provide telematics services; telematics apparatuses, namely, wireless communications devices which provide telematics services, for communicating diagnostic information with industrial assets; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for electronic monitoring and reporting of physical properties of an industrial asset using computers and sensors; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for data mining; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for electric and electronic sensor reading and data analysis; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for data automation and collection service using proprietary software to evaluate, analyze and collect service data; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for use in industrial analytics; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for use in industrial modeling; computer software for telematics apparatuses, namely, wireless communications devices which provide telematics services; computer software for setting up, configuring, maintaining, or operating telematics apparatuses, namely, wireless communications devices which provide telematics services; computer software for electronic monitoring and reporting of physical properties of an industrial asset using computers and sensors; computer software for data mining; computer software for electric and electronic sensor reading and data analysis; computer software for data automation and collection service using proprietary software to evaluate, analyze and collect service data; computer software for use in industrial analytics; computer software for use in industrial modeling; computer software for use in data science.
09 - Appareils et instruments scientifiques et électriques
Produits et services
(1) Integrated data collection and wireless transmission hardware and software for providing telematics services related to the operation and maintenance of industrial machinery; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services related to the operation and maintenance of industrial machinery; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services, for communicating diagnostic information with industrial machinery; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services, and associated computer software for electronic monitoring and reporting of physical properties of industrial machinery using computers and sensors; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services, and associated computer software for data mining, namely, collecting and analyzing data related to the operation and maintenance of industrial machinery; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services, and associated computer software for electric and electronic sensor reading and data analysis related to the operation and maintenance of industrial machinery; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services, and associated computer software for data automation and collection service using proprietary software to evaluate, analyze and collect service data related to the operation and maintenance of industrial machinery; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services, and associated computer software for use in analytics of industrial machinery to monitor and analyze operating conditions of industrial machinery; telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services, and associated computer software for use in modeling industrial machinery to make predictions related to the operating conditions of industrial machinery; computer software for telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services; computer software for setting up, configuring, maintaining, and operating telematics apparatuses, namely, integrated data collection and wireless transmission hardware and software which provide telematics services for industrial machinery; computer software for electronic monitoring and reporting of physical properties of industrial machinery using computers and sensors; computer software for data mining, namely, collecting and analyzing data related to the operation and maintenance of industrial machinery; computer software for electric and electronic sensor reading and data analysis related to the operation and maintenance of industrial machinery; computer software for data automation and collection service using proprietary software to evaluate, analyze and collect service data related to the operation and maintenance of industrial machinery; computer software for use in analytics of industrial machinery to monitor and analyze operating conditions of industrial machinery; computer software for use in modeling industrial machinery to make predictions related to the operating conditions of industrial machinery; computer software for use in predictive analytics and data science related to the operation and maintenance of industrial machinery
63.
METHOD AND SYSTEM OF IDENTIFYING ENVIRONMENT FEATURES FOR USE IN ANALYZING ASSET OPERATION
Based on an analysis of asset attribute data associated with a plurality of assets, a platform may detect a locality that is a possible instance of a given type of environment, such as a mine or construction site. In response, the platform may obtain image data associated with the detected locality and input that image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment (e.g., a boundary, navigation route, hazard, etc.), where this model is defined based on training data. Based on the likelihood data, the platform may then generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature. In turn, the platform may use the output data to simulate asset operation in the detected locality.
Based on an analysis of asset attribute data associated with a plurality of assets, a platform may detect a locality that is a possible instance of a given type of environment, such as a mine or construction site. In response, the platform may obtain image data associated with the detected locality and input that image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment (e.g., a boundary, navigation route, hazard, etc.), where this model is defined based on training data. Based on the likelihood data, the platform may then generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature. In turn, the platform may use the output data to simulate asset operation in the detected locality.
A deployment system includes a plurality of deployment environments, a change-control server, and a deployment orchestrator. Each deployment environment carries out a given phase of a deployment process for a set of artifacts. The change-control server maintains branches that correspond to respective deployment environments and that store artifacts that have been deployed to the respective deployment environments. A manifest contains a given set of artifacts stored by the change-control server, and each branch may contain multiple versions of a manifest associated with that branch. Upon creation of a new manifest version on the change-control server, the deployment orchestrator detects the presence of the new manifest version and responsively determine the differences between (i) artifacts contained in the new manifest version and (ii) artifacts deployed to a given deployment environment. Based on the determined differences, the deployment orchestrator causes one or more artifacts to be deployed to the given deployment environment.
A deployment system includes a plurality of deployment environments, a change-control server, and a deployment orchestrator. Each deployment environment carries out a given phase of a deployment process for a set of artifacts. The change-control server maintains branches that correspond to respective deployment environments and that store artifacts that have been deployed to the respective deployment environments. A manifest contains a given set of artifacts stored by the change-control server, and each branch may contain multiple versions of a manifest associated with that branch. Upon creation of a new manifest version on the change-control server, the deployment orchestrator detects the presence of the new manifest version and responsively determine the differences between (i) artifacts contained in the new manifest version and (ii) artifacts deployed to a given deployment environment. Based on the determined differences, the deployment orchestrator causes one or more artifacts to be deployed to the given deployment environment.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06F 8/71 - Gestion de versions Gestion de configuration
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; business data analysis services; data processing services. Predictive analytics and data science services in the field of operational technology; data mining; electric sensor reading and data analysis; design and development of integrated data collection and wireless transmission hardware systems and software applications; data automation and collection services using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in predictive analytics and data science services; configuration and customization of computer databases featuring information for use in predictive analytics and data science services; providing on-line non-downloadable software for use in repair or maintenance of industrial assets; providing on-line non-downloadable software for use in industrial analytics; configuration and customization of computer databases featuring information for use in industrial analytics; providing on-line non-downloadable software for use in industrial modelling; configuration and customization of computer databases featuring information for use in industrial modelling; electronic sensor reading and data analysis; providing on-line non-downloadable software for use in connection with predictive analytics and data science services in the field of operational technology, data mining, electric sensor reading and data analysis, electronic sensor reading and data analysis, integrated data collection and wireless transmission hardware systems and software applications and data automation and collection services using proprietary software to evaluate, analyze and collect service data; technical data analysis services.
Disclosed herein are systems, devices, and methods for detecting anomalies in multivariate data received from an asset-related data source, such as signal data and/or other data from an asset. According to an example, a platform may receive multivariate data from an asset in an original coordinate space and transform the data in the original coordinate space to a transformed coordinate space having a relatively fewer number of dimensions. Additionally, the platform may standardize the data in the transformed coordinate space and modify the standardized data based on a comparison between the standardized data and a set of threshold values previously defined via training data reflective of normal asset operation. Thereafter, the platform may inversely transform the modified data back to the original coordinate space and perform an analysis to detect anomalies.
Disclosed herein are systems, devices, and methods for detecting anomalies in multivariate data received from an asset-related data source, such as signal data and/or other data from an asset. According to an example, a platform may receive multivariate data from an asset in an original coordinate space and transform the data in the original coordinate space to a transformed coordinate space having a relatively fewer number of dimensions. Additionally, the platform may standardize the data in the transformed coordinate space and modify the standardized data based on a comparison between the standardized data and a set of threshold values previously defined via training data reflective of normal asset operation. Thereafter, the platform may inversely transform the modified data back to the original coordinate space and perform an analysis to detect anomalies.
Disclosed herein are systems, devices, and methods related to analyzing faults across a population of assets. In particular, examples involve receiving a selection of variables each corresponding to an asset attribute type, accessing data associated with the selected variables, determining the number of fault occurrences across the population of assets for each combination of values of the selected variables, and facilitating the identification of outlier combination(s) that correspond to an abnormally large number of fault occurrences relative to other combination(s).
Disclosed herein are systems, devices, and methods for detecting anomalies in multivariate data received from an asset-related data source, such as signal data and/or other data from an asset. According to an example, a platform may receive multivariate data from an asset in an original coordinate space and transform the data in the original coordinate space to a transformed coordinate space having a relatively fewer number of dimensions. Additionally, the platform may standardize the data in the transformed coordinate space and modify the standardized data based on a comparison between the standardized data and a set of threshold values previously defined via training data reflective of normal asset operation. Thereafter, the platform may inversely transform the modified data back to the original coordinate space and perform an analysis to detect anomalies.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
Disclosed herein are systems, devices, and methods related to analyzing faults across a population of assets. In particular, examples involve receiving a selection of variables each corresponding to an asset attribute type, accessing data associated with the selected variables, determining the number of fault occurrences across the population of assets for each combination of values of the selected variables, and facilitating the identification of outlier combination(s) that correspond to an abnormally large number of fault occurrences relative to other combination(s).
09 - Appareils et instruments scientifiques et électriques
Produits et services
Integrated data collection and wireless transmission hardware and software systems; telematics apparatuses, namely, wireless communications devices which provide telematics services; telematics apparatuses, namely, wireless communications devices which provide telematics services, for communicating diagnostic information with industrial assets; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for electronic monitoring and reporting of physical properties of an industrial asset using computers and sensors; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for data mining; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for electric and electronic sensor reading and data analysis; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for data automation and collection service using proprietary software to evaluate, analyze and collect service data; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for use in industrial analytics; telematics apparatuses, namely, wireless communications devices which provide telematics services, and associated computer software for use in industrial modeling; computer software for telematics apparatuses, namely, wireless communications devices which provide telematics services; computer software for setting up, configuring, maintaining, or operating telematics apparatuses, namely, wireless communications devices which provide telematics services; computer software for electronic monitoring and reporting of physical properties of an industrial asset using computers and sensors; computer software for data mining; computer software for electric and electronic sensor reading and data analysis; computer software for data automation and collection service using proprietary software to evaluate, analyze and collect service data; computer software for use in industrial analytics; computer software for use in industrial modeling; computer software for use in data science
74.
COMPUTER ARCHITECTURE AND METHOD FOR RECOMMENDING ASSET REPAIRS
Disclosed herein are systems, devices, and methods related for generating a recommendation to repair an asset based on operating data. A computing system may be configured maintain a hierarchy that comprises two or more distinct levels of conditions that operating data may be checked against in order to determine which repair recommendation (if any) should be output. The hierarchy may include at least (1) a first condition that corresponds to a first repair recommendation having a first level of precision, and (2) a second condition that corresponds to a second repair recommendation having a second level of precision. Once repair recommendations are identified for satisfied conditions, the computer system may select the recommendation having the highest level of precision and then cause that recommendation to be output.
Disclosed herein are systems, devices, and methods for provisioning a local analytics device to interact with a remote computing system on behalf of an asset that is coupled to the local analytics device and that is associated with a particular customer account hosted by the remote computing system.
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
H04W 4/38 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour la collecte d’informations de capteurs
H04W 4/50 - Fourniture de services ou reconfiguration de services
H04W 4/70 - Services pour la communication de machine à machine ou la communication de type machine
H04L 12/24 - Dispositions pour la maintenance ou la gestion
H04L 29/08 - Procédure de commande de la transmission, p.ex. procédure de commande du niveau de la liaison
Disclosed herein are systems, devices, and methods for provisioning a local analytics device to interact with a remote computing system on behalf of an asset that is coupled to the local analytics device and that is associated with a particular customer account hosted by the remote computing system.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing on-line non-downloadable software for use in customer relationship management (CRM), sales processes, and industrial asset management; providing on-line non-downloadable software for use in customer relationship management (CRM), namely, searching for customers, creating and accessing data and information about customers, accessing data and information about customer assets, creating and managing customer surveys, and documenting and managing issues related to customer satisfaction; providing on-line non-downloadable software for managing industrial assets; providing on-line non-downloadable software for monitoring the condition of industrial assets; providing on-line non-downloadable software for analyzing data related to industrial assets; providing on-line non-downloadable software for accessing data and information related to industrial assets; providing on-line non-downloadable software for use in the repair or maintenance of industrial assets; providing on-line non-downloadable software for use in managing the work order cycle related to the repair or maintenance of industrial assets; providing on-line non-downloadable software for use in scheduling the repair or maintenance of industrial assets; providing on-line non-downloadable software for use in managing the repair or maintenance of industrial assets; providing on-line non-downloadable software for communicating with customers about the repair or maintenance of industrial assets; providing on-line non-downloadable software for accessing predictive analytics and data science services; predictive analytics and data science services.
78.
COMPUTERIZED FLUID ANALYSIS FOR DETERMINING WHETHER AN ASSET IS LIKELY TO HAVE A FLUID ISSUE
Disclosed herein are systems, devices, and methods related to a determination of whether an asset has a fluid issue. In particular, examples involve a platform defining a predictive model for outputting an indicator of whether an asset is likely to have a fluid issue based at least on historical fluid data for one or more assets. The historical fluid data may comprise at least one of a plurality of fluid reports for the one or more assets and an indication of a fluid issue for each fluid report. The platform may receive at least one fluid report associated with a given asset and based at least on the predictive model and the received at least one fluid report, make a determination that the given asset is likely to have a fluid issue. The platform may cause a computing device to output an indication of the determination.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Collection, systematization, and synchronization of data and information into computer databases, namely, computerized database management; business data analysis services and data analysis services for increasing business productivity and improving business performance; data processing services, namely, collecting and processing data related to business productivity and business performance
(2) Predictive analytics and data science services related to the operation and maintenance of industrial machinery; predictive analytics and data science services for providing predictions and recommendations related to the operation and maintenance of industrial machinery; providing on-line non-downloadable software for use in predictive analytics and data science related to the operation and maintenance of industrial machinery; providing on-line non-downloadable software for use in predictive analytics and data science for providing predictions and recommendations related to the operation and maintenance of industrial machinery; providing on-line non-downloadable software for use in predictive analytics and data science for increasing business efficiency; configuration and customization of computer databases featuring technical information for use in repair and maintenance of industrial machinery; configuration and customization of computer databases featuring information for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning to monitor and analyze operating conditions of industrial machinery and operations; configuration and customization of computer databases featuring information for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning related to the operation and maintenance of industrial machinery; configuration and customization of computer databases featuring information for use in modeling industrial machinery and operations to make predictions related to the operating conditions of industrial machinery and operations
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services. Predictive analytics and data science services; providing on-line non-downloadable software for use in predictive analytics and data science; providing computer databases featuring information for use in predictive analytics and data science; providing computer databases featuring technical information for use in industrial analytics and industrial modelling; providing computer databases featuring technical information for use in the repair, maintenance, and monitoring of industrial assets.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services. Predictive analytics and data science services; electronic monitoring and reporting of physical properties of an industrial asset using computers and sensors; data mining; electric sensor reading and data analysis; electronic sensor reading and data analysis; design and development of integrated data collection and wireless transmission hardware systems for equipment and for software applications associated with that equipment at industrial assets; data automation and collection service using proprietary software to evaluate, analyze and collect service data; providing on-line non-downloadable software for use in connection with any one or more of the previously named services; providing on-line non-downloadable software for use in repair or maintenance of industrial assets; providing on-line non-downloadable software for use in industrial analytics; providing on-line non-downloadable software for use in industrial modelling; providing on-line non-downloadable software for use in monitoring of computer systems for security purposes; providing computer databases featuring technical information for use in industrial analytics and industrial modelling; providing computer databases featuring technical information for use in the repair and maintenance of industrial assets, industrial analytics, and industrial modelling.
82.
COMPUTER SYSTEMS AND METHODS FOR PROVIDING A VISUALIZATION OF ASSET EVENT AND SIGNAL DATA
Disclosed herein are computer systems, devices, and methods for improving the technology related to asset condition monitoring. In accordance with the present disclosure, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.
Disclosed herein are computer systems, devices, and methods for improving the technology related to asset condition monitoring.In accordance with the present disclosure, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
84.
Computer systems and methods for providing a visualization of asset event and signal data
Disclosed herein are computer systems, devices, and methods for improving the technology related to asset condition monitoring. In accordance with the present disclosure, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.
Disclosed herein are computer systems, devices, and methods for improving the technology related to asset condition monitoring.In accordance with the present disclosure, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
86.
Computer systems and methods for creating asset-related tasks based on predictive models
Computer systems, devices, and methods are provided for improving the technology related to asset condition monitoring. For instance, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.
Disclosed herein are computer systems, devices, and methods for improving the technology related to asset condition monitoring. In accordance with the present disclosure, an asset data platform may be configured to receive data related to asset operation, ingest, process, and analyze the received data, and then provide a set of advanced tools that enable a user to monitor asset operation and take action based on that asset operation. The set of advanced tools may include (1) an interactive visualization tool, (2) a task creation tool, (3) a rule creation tool, and/or (4) a metadata tool.
Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.
G01D 3/028 - Dispositions pour la mesure prévues pour les objets particuliers indiqués dans les sous-groupes du présent groupe pour atténuer les influences indésirables, p. ex. température, pression
89.
Handling of predictive models based on asset location
Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.
Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to the operation of assets. In particular, examples involve assets configured to receive and locally execute predictive models, locally individualize predictive models, and/or locally execute workflows or portions thereof.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
G06F 9/46 - Dispositions pour la multiprogrammation
Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G01D 3/08 - Dispositions pour la mesure prévues pour les objets particuliers indiqués dans les sous-groupes du présent groupe avec dispositions pour protéger l'appareil, p. ex. contre les fonctionnements anormaux, contre les pannes
G01M 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
G06F 11/00 - Détection d'erreursCorrection d'erreursContrôle de fonctionnement
G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
Disclosed herein are systems, computer-readable media, and methods related to modeling on multivariate time series data overlaid with event data. In particular, some examples involve selecting one or more historical time series data arrays similar to a recent time series data array and filtering the similar historical time series data arrays based on event data. Some examples can also involve training a localized temporal forecasting model using the filtered historical time series data arrays. Some examples can include building and/or training the localized temporal forecasting model at or near a time that a forecast is needed.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
Disclosed herein are systems, computer-readable media, and methods related to modeling on multivariate time series data overlaid with event data. In particular, some examples involve selecting one or more historical time series data arrays similar to a recent time series data array and filtering the similar historical time series data arrays based on event data. Some examples can also involve training a localized temporal forecasting model using the filtered historical time series data arrays. Some examples can include building and/or training the localized temporal forecasting model at or near a time that a forecast is needed.
Disclosed herein are systems, computer-readable media, and methods related to modeling on multivariate time series data overlaid with event data. In particular, some examples involve selecting one or more historical time series data arrays similar to a recent time series data array and filtering the similar historical time series data arrays based on event data. Some examples can also involve training a localized temporal forecasting model using the filtered historical time series data arrays. Some examples can include building and/or training the localized temporal forecasting model at or near a time that a forecast is needed.
Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
Disclosed herein is an improved local analytics device that includes a single-board computer with a high-capacity processing unit, a remote network interface configured to wirelessly communicate with a remote computing system, a local network interface configured to wirelessly communicate with a remote computing system, a secondary power source, and an asset interface that may include (i) a communication connector that enables the local analytics device to be coupled to the assets on-board systems via a single cable that carries both data and power, (ii) an asset communication subsystem configured to manage data communication with an assets on-board systems, and (iii) a power management subsystem configured to receive and manage power from the assets on-board systems.
G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
G06F 11/22 - Détection ou localisation du matériel d'ordinateur défectueux en effectuant des tests pendant les opérations d'attente ou pendant les temps morts, p. ex. essais de mise en route
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
Disclosed herein is an improved local analytics device that includes a single-board computer with a high-capacity processing unit, a remote network interface configured to wirelessly communicate with a remote computing system, a local network interface configured to wirelessly communicate with a remote computing system, a secondary power source, and an asset interface that may include (i) a communication connector that enables the local analytics device to be coupled to the asset's on-board systems via a single cable that carries both data and power, (ii) an asset communication subsystem configured to manage data communication with an asset's on-board systems, and (iii) a power management subsystem configured to receive and manage power from the asset's on-board systems.
Disclosed herein is a computer architecture and software that is configured to modify data intake operation at an asset-monitoring system based on a predictive model. In accordance with the present disclosure, the asset-monitoring system may execute a predictive model that outputs an indicator of whether at least one event from a group of events (e.g., a failure event) is likely to occur at a given asset within a given period of time in the future. Based on the output of this predictive model, the asset-monitoring system may modify one or more operating parameters for ingesting data from the given asset, such as a storage location for the ingested data, a set of data variables from the asset that are ingested, and/or a rate at which data from the asset is ingested.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Providing on-line non-downloadable software for use in customer relationship management (CRM), sales processes, and industrial machinery management; providing on-line non-downloadable software for use in customer relationship management (CRM), namely, searching for customers, creating and accessing data and information about customers, accessing data and information about customer assets, creating and managing customer surveys, and documenting and managing issues, namely, customer satisfaction; providing on-line non-downloadable software for managing industrial machinery; providing on-line nondownloadable software for monitoring the condition of industrial machinery; providing on-line nondownloadable software for analyzing data related to industrial machinery; providing on-line nondownloadable software for accessing data and information related to industrial machinery; providing on-line non-downloadable software for use in the repair and maintenance of industrial machinery; providing on-line non-downloadable software for use in managing the work order cycle, namely, the repair and maintenance of industrial machinery; providing on-line non-downloadable software for use in scheduling the repair and maintenance of industrial machinery; providing on-line non-downloadable software for use in managing the repair and maintenance of industrial machinery; providing on-line non-downloadable software for communicating with customers about the repair and maintenance of industrial machinery; providing on-line non-downloadable software for accessing predictive analytics and data science services for providing predictions and recommendations related to the operation and maintenance of industrial machinery; predictive analytics and data science services for providing predictions and recommendations related to the operation and maintenance of industrial machinery
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
collection, systematization, and synchronization of data and information into computer databases; data analysis services; data processing services; providing computer databases featuring information for use in predictive analytics and data science predictive analytics and data science services in the field of operational technology; providing on-line non-downloadable software for use in predictive analytics and data science in the field of operational technology; providing on-line non-downloadable software for use in predictive analytics and data science services; configuration and customization of computer databases featuring technical information for use in repair, maintenance, or monitoring of industrial assets; configuration and customization of computer databases featuring information for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning related to industrial assets or operations; configuration and customization of computer databases featuring information for use in industrial analytics, namely, for use in predictive analytics, data science, data mining, data collection, data analysis, data visualization, computer modeling, predictive modeling, and machine learning in the field of operational technology; configuration and customization of computer databases featuring information for use in industrial modeling, namely, for use in computer modeling of industrial assets or operations
