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Industrial system working condition identification using operation-adjusted hidden Markov model

Author

Listed:
  • Jinwen Sun

    (University of Wisconsin–Madison)

  • Akash Deep

    (University of Wisconsin–Madison)

  • Shiyu Zhou

    (University of Wisconsin–Madison)

  • Dharmaraj Veeramani

    (University of Wisconsin–Madison)

Abstract

In this article, the problem of industrial system working condition identification in the context of complex operation modes is considered. The problem is challenging due to the fact that the system dynamics are significantly affected by the operation modes. Specifically, the condition monitoring signals may behave quite differently for different operation modes. To overcome this difficulty, an operation-adjusted hidden Markov model (HMM) is proposed by combining the operation information into the construction of HMM observation models. Modeling and classification methods using the formulated HMM are provided for system condition identification under variable operation conditions. Using numerical studies and real-world data, it is demonstrated that the proposed method outperforms commonly used machine learning methods by providing more accurate condition identification results.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:6:d:10.1007_s10845-022-01942-z
    DOI: 10.1007/s10845-022-01942-z
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    References listed on IDEAS

    as
    1. Li, Jianlan & Zhang, Xuran & Zhou, Xing & Lu, Luyi, 2019. "Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model," Renewable Energy, Elsevier, vol. 132(C), pages 1076-1087.
    2. 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.
    3. Jie Ding & Vahid Tarokh & Yuhong Yang, 2018. "Model Selection Techniques -- An Overview," Papers 1810.09583, arXiv.org.
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