A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods

Abstract

Health prognosis of equipment is considered as a key process of the condition based maintenance strategy. It contributes to reduce the related risks and the maintenance costs of equipment and improve the availability, the reliability and the security of equipment. However, equipment often operates under dynamically operational and environmental conditions, and its lifetime is generally described by the monitored nonlinear time-series data. Equipment subjects to high levels of uncertainty and unpredictability so that effective methods for its online health prognosis are still in need now. This paper addresses prognostic methods based on hidden semi-Markov model (HSMM) by using sequential Monte Carlo (SMC) method. HSMM is applied to obtain the transition probabilities among health states and the state durations. The SMC method is adopted to describe the probability relationships between health states and the monitored observations of equipment. This paper proposes a novel multi-step-ahead health recognition algorithm based on joint probability distribution to recognize the health states of equipment and its health state change point. A new online health prognostic method is also developed to estimate the residual useful lifetime (RUL) values of equipment. At the end of the paper, a real case study is used to demonstrate the performance and potential applications of the proposed methods for online health prognosis of equipment.