Multiblock temporal convolution network-based temporal-correlated feature learning for fault diagnosis of multivariate processes-Reference-Cited by-同舟云学术

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Multiblock temporal convolution network-based temporal-correlated feature learning for fault diagnosis of multivariate processes

Published:2021-05 Issue: Volume:122 Page:78-84
ISSN:1876-1070
Container-title:Journal of the Taiwan Institute of Chemical Engineers
language:en
Short-container-title:Journal of the Taiwan Institute of Chemical Engineers

Author:

He Yumin,Shi Hongbo,Tan Shuai,Song Bing,Zhu Jiazhen

Funder

Natural Science Foundation of Shanghai

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Chemical Engineering,General Chemistry

Reference36 articles.

1. Dynamic process monitoring using adaptive local outlier factor;Ma;Chemometr Intel Lab,2013

2. Fault detection and classification using quality-supervised double-layer method;Song;IEEE T Ind Electron,2018

3. A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art;Liu;ISA Trans,2017

4. Deep convolutional neural network model based chemical process fault diagnosis;Wu;Comput Chem Eng,2018

5. A comparison study of basic data driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process;Yin;J Process Control,2012

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