Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.