Residual squeeze-and-excitation convolutional auto-encoder for fault detection and diagnosis in complex industrial processes-Reference-Cited by-同舟云学术

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Residual squeeze-and-excitation convolutional auto-encoder for fault detection and diagnosis in complex industrial processes

Published:2024-10 Issue: Volume:136 Page:108872
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Yu Jianbo,Li Shijin^ORCID,Liu Xing,Li Hao,Ma Mingyan,Liu Peilun,You Lichun

Funder

Fundamental Research Funds for the Central Universities

National Engineering and Research Center for Commercial Aircraft Manufacturing

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference56 articles.

1. Nonlinear chemical processes fault detection based on adaptive kernel principal component analysis;Chen;Systems Science & Control Engineering,2020

2. One-dimensional Convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes;Chen;J. Process Control,2020

3. Slow feature analysis-aided detection and diagnosis of incipient faults for running gear systems of high-speed trains;Cheng;ISA (Instrum. Soc. Am.) Trans.,2022

4. Fault diagnosis based on Fisher discriminant analysis and support vector machines;Chiang;Comput. Chem. Eng.,2004

5. From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis;Dai;IEEE Trans. Ind. Inf.,2013

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