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

Jinwen Sun, Akash Deep, Shiyu Zhou () and Dharmaraj Veeramani
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Jinwen Sun: University of Wisconsin–Madison
Akash Deep: University of Wisconsin–Madison
Shiyu Zhou: University of Wisconsin–Madison
Dharmaraj Veeramani: University of Wisconsin–Madison

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 6, No 7, 2624 pages

Abstract: 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.

Keywords: HMM; Condition identification; Operation mode; Observation model (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-022-01942-z

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