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Multi-grained mode partition and robust fault diagnosis for multimode industrial processes

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  • Zhou, Han
  • Yin, Hongpeng
  • Chai, Yi

Abstract

Practical industrial processes usually operate under multiple conditions to meet the requirements of manufacturing strategies. A general choice is to partition data according to the number of operating modes and then develop learning models to suit each mode. However, data from a same mode still exhibit multi-grained patterns due to the non-Gaussian processes, occurrence of faults, etc. Only discovering coarse between-mode correlations may fail to achieve precise mode partition, resulting in unsatisfied diagnosis performance. To this end, this paper presents a novel method for multimode industrial processes fault diagnosis. Firstly, the hierarchical clustering strategy exploits the multi-grained information of process data, modeling both the between-mode (different operation conditions) and within-mode correlations (patterns in each mode). Then, a feature learning algorithm based on nonnegative matrix factorization (NMF) is proposed to learn data features, allowing a sample to be represented by the discovered multi-grained structural information. A weighted metric is also designed to reasonably measure the feature similarities learned by the NMF. Particularly in our framework, a â„“p-norm (0

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  • Zhou, Han & Yin, Hongpeng & Chai, Yi, 2023. "Multi-grained mode partition and robust fault diagnosis for multimode industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006263
    DOI: 10.1016/j.ress.2022.109011
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    References listed on IDEAS

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    1. Fu, Xiuwen & Li, Qing & Li, Wenfeng, 2023. "Modeling and analysis of industrial IoT reliability to cascade failures: An information-service coupling perspective," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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