文献类型：期刊文献

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

作者：Zhenhua Yu[1];Zongyu Yao[1];Weijun Wang[1];Qingchao Jiang[1];Zhixing Cao[1]

机构：[1]Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China

年份：2025

卷号：84

期号：8

起止页码：146

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报(英文版)

基金：support from the following foundations:the National Natural Science Foundation of China(62322309,62433004);Shanghai Science and Technology Innovation Action Plan(23S41900500);Shanghai Pilot Program for Basic Research(22TQ1400100-16).

语种：英文

中文关键词：Industrial process;Bioprocess;Fault diagnosis;Neural networks;Fermentation

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.