4.7 Article

Minimalist module analysis for fault detection and localization

期刊

SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41598-021-02676-3

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资金

  1. National Natural Science Foundation of China [62003220]
  2. Natural Science Foundation of Shenzhen, China [JCYJ20190809114009697]
  3. Young Talents by Department of Education of Guangdong Province, China [2020KQNCX204]

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A new method called minimalist module analysis (MMA) is proposed in this study to address the issue of redundant correlations between process variables in traditional multivariate statistical-based process monitoring (MSPM) methods. Simulation tests demonstrate that MMA achieves superior performance in fault detection and localization.
Traditional multivariate statistical-based process monitoring (MSPM) methods are effective data-driven approaches for monitoring large-scale industrial processes, but have a shortcoming in handling the redundant correlations between process variables. To address this shortcoming, this study proposes a new MSPM method called minimalist module analysis (MMA). MMA divides process data into several different minimalist modules and one more independent module. All variables in the minimalist module are strongly correlated, and no redundant variables exist; therefore, the extracted feature components in one minimalist module will not be disturbed by noise from the other modules. This study also proposes new monitoring indices and a fault localization strategy for MMA, and simulation tests demonstrate that MMA achieves superior performance in fault detection and localization.

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