SmdaNet: A hierarchical hard sample mining and domain adaptation neural network for fault diagnosis in industrial process

doi:10.1016/j.cjche.2025.05.003

中国化学工程学报 ›› 2025, Vol. 84 ›› Issue (8): 146-157.DOI: 10.1016/j.cjche.2025.05.003

• Full Length Article • 上一篇下一篇

SmdaNet: A hierarchical hard sample mining and domain adaptation neural network for fault diagnosis in industrial process

Zhenhua Yu, Zongyu Yao, Weijun Wang, Qingchao Jiang, Zhixing Cao

Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2024-12-30 修回日期:2025-05-08 接受日期:2025-05-08 出版日期:2025-08-28 发布日期:2025-05-22
通讯作者: Qingchao Jiang,E-mail:qchjiang@ecust.edu.cn;Zhixing Cao,E-mail:zcao@ecust.edu.cn
基金资助:
The authors gratefully acknowledge the support from the following foundations: the National Natural Science Foundation of China (62322309, 62433004), Shanghai Science and Technology Innovation Action Plan (23S41900500), and Shanghai Pilot Program for Basic Research (22TQ1400100-16).

SmdaNet: A hierarchical hard sample mining and domain adaptation neural network for fault diagnosis in industrial process

Zhenhua Yu, Zongyu Yao, Weijun Wang, Qingchao Jiang, Zhixing Cao

Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2024-12-30 Revised:2025-05-08 Accepted:2025-05-08 Online:2025-08-28 Published:2025-05-22
Contact: Qingchao Jiang,E-mail:qchjiang@ecust.edu.cn;Zhixing Cao,E-mail:zcao@ecust.edu.cn
Supported by:
The authors gratefully acknowledge the support from the following foundations: the National Natural Science Foundation of China (62322309, 62433004), Shanghai Science and Technology Innovation Action Plan (23S41900500), and Shanghai Pilot Program for Basic Research (22TQ1400100-16).

摘要/Abstract

摘要： Fault diagnosis in industrial process is essential for ensuring production safety and efficiency. However, existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains, resulting in suboptimal performance and robustness. Therefore, this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive (SmdaNet). First, the method uses deep belief networks (DBN) to build a diagnostic model. Hard samples are mined based on the loss values, dividing the data set into hard and easy samples. Second, elastic weight consolidation (EWC) is used to train the model on hard samples, effectively preventing information forgetting. Finally, the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions. Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy, robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.

关键词: Industrial process, Bioprocess, Fault diagnosis, Neural networks, Fermentation

Abstract: Fault diagnosis in industrial process is essential for ensuring production safety and efficiency. However, existing methods exhibit limited capability in recognizing hard samples and struggle to maintain consistency in feature distributions across domains, resulting in suboptimal performance and robustness. Therefore, this paper proposes a fault diagnosis neural network for hard sample mining and domain adaptive (SmdaNet). First, the method uses deep belief networks (DBN) to build a diagnostic model. Hard samples are mined based on the loss values, dividing the data set into hard and easy samples. Second, elastic weight consolidation (EWC) is used to train the model on hard samples, effectively preventing information forgetting. Finally, the feature space domain adaptation is introduced to optimize the feature space by minimizing the Kullback–Leibler divergence of the feature distributions. Experimental results show that the proposed SmdaNet method outperforms existing approaches in terms of classification accuracy, robustness and interpretability on the penicillin simulation and Tennessee Eastman process datasets.

Key words: Industrial process, Bioprocess, Fault diagnosis, Neural networks, Fermentation

Zhenhua Yu, Zongyu Yao, Weijun Wang, Qingchao Jiang, Zhixing Cao. SmdaNet: A hierarchical hard sample mining and domain adaptation neural network for fault diagnosis in industrial process[J]. 中国化学工程学报, 2025, 84(8): 146-157.

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SmdaNet: A hierarchical hard sample mining and domain adaptation neural network for fault diagnosis in industrial process

SmdaNet: A hierarchical hard sample mining and domain adaptation neural network for fault diagnosis in industrial process

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