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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Vydáno v:IEEE transactions on industrial informatics Ročník 20; číslo 5; s. 7545 - 7555
Hlavní autoři: Liu, Lang, Zheng, Ying, Liang, Shaojun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 specific fault by its deviation factor and introduces fault label information during pretraining procedure. Then, temporal autoencoder (TAE) is designed to capture the temporal and spatial feature synchronously and model the complex dependencies of dynamic samples. The stacked TAE is built to enhance the ability of feature extraction by combining multiple TAEs. By inputting the sorted variables sequentially, the VW-STAE is trained as a binary classifier for a specific fault; thereby its input variables and the corresponding network parameters are ultimately selected according to the VW-STAE with the optimal diagnosis performance. Finally, a bank of VW-STAEs is adopted for all faults, which is followed by a fully connected layer to achieve comprehensive fault diagnosis result. The effectiveness of the proposed method is demonstrated in the sensorless drive diagnosis example. The results indicate that the proposed method outperforms other existing deep learning methods.
Bibliografie:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3353921