HMM-Based Joint Modeling of Condition Monitoring Signals and Failure Event Data for Prognosis

Abstract

Accurate estimation of remaining useful life (RUL) of a unit is critical to fulfill reliability commitments. In the presence of hard failures (i.e., absence of a predefined failure threshold), accurate prognosis of RUL using condition monitoring (CM) signals becomes challenging. To tackle this problem, we present a prognostic framework by jointly modeling CM signals and failure event data. Development of the presented method depends on the idea that while the unit operates, it continually degrades through a series of hidden states and the CM signals are functionally related to this hidden failure process. The unit fails once the hidden failure process reaches a dead state. Through this modeling, requirement of a failure threshold on CM signals is eliminated. We provide a modified expectation-maximization procedure to estimate parameters, and through a comprehensive set of numerical as well as real-world experiments, we demonstrate superior prognosis performance against some benchmark methods.