文献类型：期刊文献

英文题名：Fault Diagnostic Method Based on Deep Learning and Multimodel Feature Fusion for Complex Industrial Processes

作者：Li, Zhichao[1];Tian, Li[1];Jiang, Qingchao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2020

卷号：59

期号：40

起止页码：18061

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20204709515167);WOS:【SCI-EXPANDED(收录号:WOS:000580512300043)】；

基金：The authors are grateful for the support of the National Natural Science Foundation of China (21878081) and Fundamental Research Funds for the Central Universities under the grant of China (222201917006).

语种：英文

外文关键词：Learning systems - Industrial research - Fault detection - Benchmarking

摘要：Fault diagnostic methods based on deep learning for industrial processes are becoming a research hotspot. Most existing methods focus on algorithmic improvements and attempt to establish a single model to extract effective features of faults. However, effective information related to different faults is diverse. Therefore, instead of using a single model to extract features and build a model to correctly diagnose all types of faults, we propose a novel fault diagnostic method based on deep learning and multimodel feature fusion. First, the minimum redundancy-maximum relevance method is used to select the variables that are the most relevant to each fault. Next, the features of each fault are extracted using a stack autoencoder, and the corresponding residual matrices are obtained. The features and residuals obtained using each model are then spliced as new inputs to establish a classifier for fault diagnosis. Finally, we apply the proposed method to the Tennessee Eastman benchmark process to demonstrate its performance and efficiency.