A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis

Wenzhu Liao (), Dan Li and Shihao Cui
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Wenzhu Liao: Chongqing University
Dan Li: Chongqing University
Shihao Cui: Chongqing University

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 8, No 10, 1845-1857

Abstract: Abstract This paper proposes a novel hidden Markov model (HMM) based on simulated annealing (SA) algorithm and expectation maximization (EM) algorithm for machinery diagnosis. As traditional HMM is sensitive to initial values and EM is easy to trap into a local optimization, SA is combined to improve HMM which can overcome local optimization searching problem. The proposed HMM has strong ability of global convergence, and optimizes the process of parameters estimation. Finally, through a case study, the computation results illustrate this SAEM-HMM has high efficiency and accuracy, which could help machinery diagnosis in practical.

Keywords: Hidden Markov model; Expectation maximization; Simulated annealing; Diagnosis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-016-1222-1

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