Uptake Technologies, Inc.

Future Wei Technologies, Inc.

Tuo Li

James Herzog

Zhibi Wang

Michael Horrell

Benedict Augustine

Brian Burns

Frank Fineis

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

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

A system and method of improving anomaly detection rate in a communication network. A server computer may receive a data set comprising traffic flows communicated over the communication network and group the traffic flows into data categories based on the type of network service such as transport control protocol (TCP) port numbers or User Datagram Protocol (UDP) port numbers of the traffic flows, or based on application layer protocols associated with the traffic flows. The server computer may further detect anomalies in each of the data categories based on inconsistencies between at least one common feature associated with a data category and traffic flows in the data category. Different data categories may be associated with different the at least one common feature. The anomaly detection may be supervised or unsupervised.

Methodology to improve the anomaly detection rate

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.

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Chicago, IL, United States of America

Tuo Li