Concurrent nonstationary process analysis model and its application in nonstationary process monitoring

Yun Wang (), Guang Chen (), Yuchen He (), Lijuan Qian (), Ping Wu () and Lingjian Ye ()
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Yun Wang: Zhejiang Tongji Vocational College of Science and Technology
Guang Chen: China Jiliang University
Yuchen He: China Jiliang University
Lijuan Qian: China Jiliang University
Ping Wu: ZheJiang Sci-Tech University
Lingjian Ye: Huzhou University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 8, No 24, 5757-5778

Abstract: Abstract Over the past decades, effective monitoring and high product quality of nonstationary process have been considered as challenging tasks due to its sophisticated data characteristics. In this paper, a concurrent nonstationary process analysis (CNPA) strategy is proposed for nonstationary process monitoring. Firstly, the original data space is decomposed into several subspaces according to the nonstationary characteristics and their relationship with quality variables. Secondly, corresponding latent variables are developed to describe the behavior of each subspace, which will then be applied in process monitoring. The efficacy of the proposed method is confirmed by a numerical case and an application in penicillin fermentation process. Compared with previous researches, this method exhibits better performance in fault detection for nonstationary processes.

Keywords: Nonstationary process; Fault detection; Concurrent nonstationary process analysis (CNPA); EM algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02516-x

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