An Integrated Detection-Prognostics Methodology for Components with Intermittent Faults

Abstract

Some industrial components, such as valves, relay switches, and motors occasionally experience intermittent faults (IFs) that usually disappear without any repair or intervention. This phenomenon occurs at a relatively low frequency even in components that are in an “as good as new” state. However, an increase in the frequency of IFs often indicates the onset of degradation. We develop an integrated detection-prognostics model for components that exhibit IFs and whose degradation data is high-dimensional. We discuss the use of Dynamic Time Warping (DTW) and a Variational Autoencoder (VAE) to perform feature engineering on the data. We then propose a Hidden Markov Model (HMM) based monitoring strategy composed of two parts: (1) a detection model that tracks and flags changes in the intermittent fault frequency (IFF), and (2) a prognostic model that leverages how the transition probabilities of the HMM evolve with progressive degradation to compute the remaining life distribution (RLD) of the component. We examine the performance of our modeling framework using high-dimensional data generated from a vehicular electrical system testbed designed to accelerate the degradation of a vehicle starter motor.