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

Author

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  • Wenzhu Liao

    (Chongqing University)

  • Dan Li

    (Chongqing University)

  • Shihao Cui

    (Chongqing University)

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.

Suggested Citation

  • Wenzhu Liao & Dan Li & Shihao Cui, 2018. "A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1845-1857, December.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:8:d:10.1007_s10845-016-1222-1
    DOI: 10.1007/s10845-016-1222-1
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    References listed on IDEAS

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    1. Ershun Pan & Wenzhu Liao & Lifeng Xi, 2012. "A single machine-based scheduling optimisation model integrated with preventive maintenance policy for maximising the availability," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 10(4), pages 451-469.
    2. Dong, Ming & He, David, 2007. "Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis," European Journal of Operational Research, Elsevier, vol. 178(3), pages 858-878, May.
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    Cited by:

    1. Jinwen Sun & Akash Deep & Shiyu Zhou & Dharmaraj Veeramani, 2023. "Industrial system working condition identification using operation-adjusted hidden Markov model," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2611-2624, August.
    2. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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