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Health assessment and prognostics based on higher‐order hidden semi‐Markov models
Author(s) -
Liao Ying,
Xiang Yisha,
Wang Min
Publication year - 2021
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/nav.21947
Subject(s) - prognostics , hidden markov model , unobservable , hidden semi markov model , benchmark (surveying) , computer science , markov model , inference , gibbs sampling , machine learning , artificial intelligence , markov chain , data mining , econometrics , variable order markov model , bayesian probability , mathematics , geodesy , geography
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 the HOHSMM. We conduct a simulation study to evaluate the performance of the proposed HOHSMM sampler and examine the impacts of the distant‐history dependency. We design a decoding algorithm to estimate the hidden health states using the learned model. Remaining useful life 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 National Aeronautics and Space Administration (NASA) turbofan engines. We further compare the RUL prediction performance between the proposed HOHSMM and a benchmark mixture of Gaussians HMM prognostics method. The results show that the HOHSMM‐based prognostics framework provides good hidden health‐state assessment and RUL estimation for complex systems.