Fault detection based on multifractal analysis and variational autoencoder for complex industrial process-Reference-Cited by-同舟云学术

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Fault detection based on multifractal analysis and variational autoencoder for complex industrial process

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
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Author:

Yu Feng¹,Yu Miao²,Li Xu³,Liu Dongming⁴,Zhang Qi¹

Affiliation:

1. Shenyang University of Chemical Technology,College of Information Engineering,Shenyang,China

2. Northeastern University at Qinhuangdao,School of Control Engineering,Qinhuangdao,China

3. Nanjing University of Posts and Telecommunications,School of Modern Posts,Nanjing,China

4. Shenyang Jianzhu University,School of Electrical and Control Engineering,Shenyang,China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295660.pdf?arnumber=10295660

Reference15 articles.

1. Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes

2. Weighted time series fault diagnosis based on a stacked sparse autoencoder

3. A plant-wide industrial process control problem

4. Design teacher and supervised dual stacked auto-encoders for quality-relevant fault detection in industrial process

5. Multifractal analysis of stack voltage based on wavelet leaders: a new tool for the on-line diagnosis of PEMFC;benouioua;Fuel Cells,2015

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