Variable-Wise Stacked Temporal Autoencoder for Intelligent Fault Diagnosis of Industrial Systems

Fault diagnosis of dynamic multivariate systems is a challenging problem. In this article, a novel fault diagnosis scheme based on variable-wise stacked temporal autoencoder (VW-STAE) is proposed. First, a variable-wise strategy is proposed on the raw industrial data, which sorts the variables for a...

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Bibliographic Details

Published in:	IEEE transactions on industrial informatics Vol. 20; no. 5; pp. 7545 - 7555
Main Authors:	Liu, Lang, Zheng, Ying, Liang, Shaojun
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Data models Decoding Deep learning Fault diagnosis Feature extraction Machine learning Training variable selection variable-wise stacked temporal autoencoder (VW-STAE) Variables
ISSN:	1551-3203, 1941-0050
Online Access:	Get full text
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