Uptake Technologies, Inc.

Michael Horrell

Adam McElhinney

John Ciasulli

Sheng Zhong

Jason Kolb

Adam Mcelhinney

James Herzog

Brad Nicholas

Tuo Li

Brian Silva

Tyler Roberts

Frank Fineis

Dennis Liang

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.

Computer systems and methods for creating asset-related tasks based on predictive models

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.

Computer system and method for distributing execution of a predictive model

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.

Computer system and method for defining and using a predictive model configured to predict asset failures

Dynamic execution of predictive models

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

Disclosed herein are systems, devices, and methods related to assets and asset operating conditions. In particular, examples involve defining and executing predictive models for outputting health metrics that estimate the operating health of an asset or a part thereof, analyzing health metrics to determine variables that are associated with high health metrics, and modifying the handling of abnormal-condition indicators in accordance with a prediction of a likely response to such abnormal-condition indicators, among other examples.

Asset health score

Subsystem health score

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, 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.

Adaptive handling of abnormal-condition indicator criteria

Examples of systems, devices, and methods related to assets and asset operating conditions involve determining health metrics that estimate the operating health of an asset or a part thereof, 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.

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Chicago, IL, United States of America

Michael Horrell