Diagnostic fault detection using multivariate statistical pattern library

Abstract

A method for detecting early indications of equipment failure in an industrial system includes receiving sensor training data collected from industrial equipment under normal conditions and identifying periods of time in the sensor training data when the equipment was functioning normally; finding a pattern for each identified period of time to initialize a plurality of mixture models; learning weighting factors, mean and variance of each of the plurality of mixture models, and removing unimportant models from the plurality of mixture models; determining a Gaussian Markov random field model from surviving mixture models by calculating gating functions for each of the variables and individual mixture models; determining a threshold value of an anomaly score for each variable from the sensor training data; and deploying the model to monitor sensor data from industrial equipment using the threshold values to detect anomalous sensor data values indicative of an impending system failure.