General Electric Company

Abhay Harpale

Hao Huang

Achalesh Kumar Pandey

Helena Goldfarb

A method of detecting status changes, and a corresponding point-in-time, in monitored entities, includes receiving one or more elements of time-series data from one or more sensors, the elements of time-series data representing an operational state of the monitored entity, creating a predictive model from the time-series data in a datastore memory, applying a transduction classifier to the predictive model, the transduction classifier detecting a change from healthy to unhealthy in the time-series data, and the corresponding point-in-time when the change occurred, and providing an identification of the change in the time-series data and the corresponding point-in-time. In some embodiments the transduction classifier can be a maximum margin classifier having a support vector machine component and a temporal transductive component. A system and a non-transitory computer readable medium are also disclosed.

Max-margin temporal transduction for automatic prognostics, diagnosis and change point detection

According to some embodiments, a system and method are provided to create a template associated with an industrial problem. The method comprises receiving one or more kernels from a machine learning library. The one or more kernels are then aggregated, via a processor, into a template. The template is integrated into a user interface where is may be executed.

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San Ramon, CA, United States of America

Abhay Harpale