NEAR REAL-TIME DETECTION AND CLASSIFICATION OF MACHINE ANOMALIES USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

摘要

A method of determining anomalous operation of a system includes: capturing a stream of data representing sensed (or determined) operating parameters of the system over a range of operating states, with a stability indicator representing whether the system was operating in a stable state when the operating parameters were sensed; determining statistical properties of the stream of data, including an amplitude-dependent parameter and a variance thereof over time parameter for an operating regime representing stable operation; determining a statistical norm for the statistical properties that distinguish between normal operation and anomalous operation of the system; responsive to detecting that normal and anomalous operation of the system can no longer be reliably distinguished, determining new statistical properties to distinguish between normal and anomalous system operation; and outputting a signal based on whether a concurrent stream of data representing sensed operating parameters of the system represent anomalous operation of the system.