文献类型：期刊文献

英文题名：Multiblock temporal convolution network-based temporal-correlated feature learning for fault diagnosis of multivariate processes

作者：He, Yumin[1];Shi, Hongbo[1];Tan, Shuai[1];Song, Bing[1];Zhu, Jiazhen[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：122

起止页码：78

外文期刊名：JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS

收录：;EI(收录号：20212210430105);WOS:【SCI-EXPANDED(收录号:WOS:000657786900009)】；

基金：This research is supported by the National Natural Science Foundation of China (No. 62073140, 62073141) ; National Natural Science Foundation of Shanghai (No. 19ZR1473200) .

语种：英文

外文关键词：Convolutional neural network; Deep learning; Temporal-correlation; Fault diagnosis

摘要：A new temporal-correlated feature learning method, multiblock temporal convolutional network (MBTCN), is proposed for supervised fault diagnosis of multivariate processes in this paper. The MBTCN used a new idea, "local extraction and global integration," to consider the cross-correlation and the temporal-correlation in the multivariate processes' data. First, MBTCN divides the overall variables into several sub-blocks based on process mechanisms and uses one-dimensional convolutional neural network (1D-CNN) architectures to extract temporal-correlated features in each sub-block, with the 1D-CNN network sliding over time steps. Thus, the adjacent samples and the close-related variables can be considered together in the network. Then, MBTCN constructs a global feature representation built by concatenating local features of sub-blocks. Besides, in the supervised training phase, training labels are modified by label smoothing technology to alleviate the overfitting. Finally, a Tennessee Eastman (TE) process is used to test the proposed model's effectiveness. The code of MBTCN can be found in https://github.com/heyumi0901/MBTCN. (c) 2021 Published by Elsevier B.V. on behalf of Taiwan Institute of Chemical Engineers.