Uptake Technologies, Inc.

Smartsignal Corporation

Ge Intelligent Platforms Inc.

Intellectual Assets LLC

James Herzog

Tuo Li

Randall L Bickford

Stephan W Wegerich

Benedict Augustine

Michael Horrell

Robert Matthew Pipke

Andre Wolosewicz

Xiao Xu

Brian Burns

Frank Fineis

Devang Jagdish Gandhi

Eric Hall

Yuan Tang

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.

Computer system and method for detecting anomalies in multivariate data

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.

Localized temporal model forecasting

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.

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 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.

Computer system and method for creating a supervised failure model

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.

Systems and methods for detecting and remedying software 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.

Detection of anomalies in multivariate data

A vector of actual sensor values is received. A maturity of a model is determined and the maturity is defined for sensors. A function that translates model maturity to model range inhibition measure is determined. A model range inhibition (MRI) measure is determined. An MRI limit based upon the MRI measure is determined. The received vector is compared to the MRI limit and the model is selectively changed based upon the comparing. In other aspects, vectors are received having actual values of driver and response sensors. A function that provides a set of boundaries between acceptable observations and unacceptable observations is also determined. Measures of similarity between vectors are determined. The measures of similarity and the function are compared and the model is selectively changed based upon the comparing.

Apparatus and method for model adaptation

A method for determining the future operational condition of an object includes obtaining reference data that indicates the normal operational state of the object, and obtaining input pattern arrays. Each input pattern array has a plurality of input vectors, while each input vector represents a time point and has input values representing a plurality of parameters indicating the current condition of the object. At least one processor generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. The estimate values, in the form of an estimate matrix, include at least one estimate vector of inferred estimate values, and represents at least one time point that is not represented by the input vectors. The inferred estimate values are used to determine a future condition of the object.

Method of sequential kernel regression modeling for forecasting and prognostics

A monitoring system for determining the future operational condition of an object includes an empirical model to receive reference data that indicates the normal operational state of the object and input pattern arrays. Each input pattern array has a plurality of input vectors, while each input vector represents a time point and has input values representing a plurality of parameters indicating the current condition of the object. The model generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. The estimate values, in the form of an estimate matrix, include at least one estimate vector of inferred estimate values, and represents at least one time point that is not represented by the input vectors. The inferred estimate values are used to determine a future condition of the object.

System of sequential kernel regression modeling for forecasting and prognostics

A monitoring system for determining the future behavior of a financial system includes an empirical model to receive reference data that indicates the normal behavior of the system and input pattern arrays. Each input pattern array has a plurality of input vectors, while each input vector represents a time point and has input values representing a plurality of parameters indicating the current condition of the system. The model generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. The estimate values, in the form of an estimate matrix, include at least one estimate vector of inferred estimate values, and represents at least one time point that is not represented by the input vectors. The inferred estimate values are used to determine a future behavior of the financial system.

System of sequential kernel regression modeling for forecasting financial data

A monitoring system for determining the condition of an object includes an empirical model with reference data that indicates the normal operational state of the object, and input pattern arrays. Each input pattern array has a plurality of time-ordered input vectors, while each input vector has input values representing a plurality of parameters indicating the current condition of the object. The empirical model generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. A differencing module compares the estimates values to the corresponding input values so that resulting values from the comparison can be used to determine the condition of the object.

Monitoring system using kernel regression modeling with pattern sequences

A method for monitoring the condition of an object includes obtaining reference data that indicates the normal operational state of the object, and obtaining input pattern arrays. Each input pattern array has a plurality of time-ordered input vectors, while each input vector has input values representing a plurality of parameters indicating the current condition of the object. Then at least one processor generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. The estimate values are compared to the corresponding input values so that resulting values from the comparison can be used to determine the condition of the object.

Monitoring method using kernel regression modeling with pattern sequences

A method and apparatus are provided for diagnosing faults in a monitored system that is monitored by sensors. An empirical model is generated for a targeted component of the monitored system. The empirical model is trained with an historical data source that contains example observations of the sensors. Substantially real-time estimates are generated based on instrumented data corresponding to the targeted component. The substantially real-time estimates are compared and differenced with instrumented readings from the sensors to provide residual values. The residual values are analyzed to detect the faults and determine a location of the faults in the monitored system.

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Downers Grove, IL, United States of America

James Herzog