Supervised convolutional autoencoder-based fault-relevant feature learning for fault diagnosis in industrial processes-Reference-Cited by-同舟云学术

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Supervised convolutional autoencoder-based fault-relevant feature learning for fault diagnosis in industrial processes

Published:2022-03 Issue: Volume:132 Page:104200
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:

Yu Feng,Liu Jianchang,Liu Dongming,Wang Honghai

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Chemical Engineering,General Chemistry

Reference36 articles.

1. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes;Jiang;Ind Eng Chem Res,2019

2. Review on data-driven modeling and monitoring for plant-wide industrial processes;Ge;Chemom Intell Lab Syst,2017

3. Machine learning technique for data-driven fault detection of nonlinear processes;Said;J Intell Manuf,2020

4. Quality-Relevant Fault Monitoring Based on Locally Linear Embedding Orthogonal Projection to Latent Structure;Zhou;Ind Eng Chem Res,2019

5. Fault diagnosis of bearing damage by means of the linear discriminant analysis of stator current features from the frequency selection;Mboo;IEEE Trans Ind Appl,2016

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