2020/02/12 by Ying Liao, Liao, Ying, Yisha Xiang +3
Computer Science · Engineering · #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Fault Detection and Control Systems #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.2002.05272
openalex publication_date 2020/02/12 · openalex created_date 2020/02/24 · openalex updated_date 2026/08/04
This paper presents a new and flexible prognostics framework based on a higher order hidden semi-Markov model (HOHSMM) for systems or components with unobservable health states and complex transition dynamics. The HOHSMM extends the basic hidden Markov model (HMM) by allowing the hidden state to depend on its more distant history and assuming generally distributed state duration. An effective Gibbs sampling algorithm is designed for statistical inference of an HOHSMM. The performance of the proposed HOHSMM sampler is evaluated by conducting a simulation experiment. We further design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life (RUL) is predicted using a simulation approach given the decoded hidden states. The practical utility of the proposed prognostics framework is demonstrated by a case study on NASA turbofan engines. The results show that the HOHSMM-based prognostics framework provides good hidden health state assessment and RUL estimation for complex systems.