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

Abstract:

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.