Model parameter estimation and residual life prediction for a partially observable failing system

• Published in: Naval research logistics Vol. 62; no. 3; pp. 190 - 205
• Main Authors: Khaleghei, Akram, Makis, Viliam
• Format: Journal Article
• Language: English
• Published: New York Blackwell Publishing Ltd 01.04.2015; Wiley Subscription Services, Inc
• Subjects: Algorithms; Condition monitoring; condition-based maintenance; expectation-maximization algorithm; Failure; hidden semi-Markov model; Logistics; Markov chains; Mathematical analysis; Mathematical models; mean residual life; Naval; reliability; Warning
• ISSN: 0894-069X; 1520-6750
• DOI: 10.1002/nav.21622

We consider a partially observable degrading system subject to condition monitoring and random failure. The system's condition is categorized into one of three states: a healthy state, a warning state, and a failure state. Only the failure state is observable. While the system is operational, vector data that is stochastically related to the system state is obtained through condition monitoring at regular sampling epochs. The state process evolution follows a hidden semi‐Markov model (HSMM) and Erlang distribution is used for modeling the system's sojourn time in each of its operational states. The Expectation‐maximization (EM) algorithm is applied to estimate the state and observation parameters of the HSMM. Explicit formulas for several important quantities for the system residual life estimation such as the conditional reliability function and the mean residual life are derived in terms of the posterior probability that the system is in the warning state. Numerical examples are presented to demonstrate the applicability of the estimation procedure and failure prediction method. A comparison results with hidden Markov modeling are provided to illustrate the effectiveness of the proposed model. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 190–205, 2015