Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism

Applying deep learning to unsupervised bearing fault diagnosis in complex industrial environments is challenging. Traditional fault detection methods rely on labeled data, which is costly and labor-intensive to obtain. This paper proposes a novel unsupervised approach, WDCAE-LKA, combining a wide ke...

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Vydané v:Sensors (Basel, Switzerland) Ročník 24; číslo 24; s. 8053
Hlavní autori: Yan, Hao, Si, Xiangfeng, Liang, Jianqiang, Duan, Jian, Shi, Tielin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 01.12.2024
Predmet:
Accuracy
Adaptability
adaptive thresholding
Artificial intelligence
auto-encoder
Bearings
Clustering
Datasets
Deep learning
Efficiency
Embedded systems
Fault diagnosis
kernelized attention
Machinery
machinery fault detection
Neural networks
Systems stability
unsupervised feature learning
Wavelet transforms
China
auto-encoder
kernelized attention
machinery fault detection
adaptive thresholding
unsupervised feature learning
ISSN:1424-8220, 1424-8220
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Shrnutí:Applying deep learning to unsupervised bearing fault diagnosis in complex industrial environments is challenging. Traditional fault detection methods rely on labeled data, which is costly and labor-intensive to obtain. This paper proposes a novel unsupervised approach, WDCAE-LKA, combining a wide kernel convolutional autoencoder (WDCAE) with a large kernel attention (LKA) mechanism to improve fault detection under unlabeled conditions, and the adaptive threshold module based on a multi-layer perceptron (MLP) dynamically adjusts thresholds, boosting model robustness in imbalanced scenarios. Experimental validation on two datasets (CWRU and a customized ball screw dataset) demonstrates that the proposed model outperforms both traditional and state-of-the-art methods. Notably, WDCAE-LKA achieved an average diagnostic accuracy of 90.29% in varying fault scenarios on the CWRU dataset and 72.89% in the customized ball screw dataset and showed remarkable robustness under imbalanced conditions; compared with advanced models, it shortens training time by 10–26% and improves average fault diagnosis accuracy by 5–10%. The results underscore the potential of the WDCAE-LKA model as a robust and effective solution for intelligent fault diagnosis in industrial applications.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24248053