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• W3165271490 abstract "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." @default.
• W3165271490 created "2021-06-07" @default.
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• W3165271490 date "2021-05-01" @default.
• W3165271490 modified "2023-10-16" @default.
• W3165271490 title "Multiblock temporal convolution network-based temporal-correlated feature learning for fault diagnosis of multivariate processes" @default.
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• W3165271490 doi "https://doi.org/10.1016/j.jtice.2021.04.062" @default.
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