Unsupervised learning of part-based representations using sparsity optimized auto-encoder for machinery fault diagnosis

Zhang, Zhiqiang; Shen, Yuxiang; Xu, Shuiqing

doi:10.1016/j.conengprac.2024.105871

Control Engineering Practice

2024

DOI: 10.1016/j.conengprac.2024.105871

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Unsupervised learning of part-based representations using sparsity optimized auto-encoder for machinery fault diagnosis

Zhiqiang Zhang,

Yuxiang Shen,

Shuiqing Xu

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

Yan,

Si,

Liang

et al. 2024

Sensors

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

Yan,

Si,

Liang

et al. 2024

Sensors

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show abstract

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Unsupervised learning of part-based representations using sparsity optimized auto-encoder for machinery fault diagnosis

Cited by 1 publication

References 36 publications

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

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

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