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A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks

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  • Yang, Zhe
  • Baraldi, Piero
  • Zio, Enrico

Abstract

In multi-component systems, degradation, maintenance, renewal and operational mode change continuously the operating conditions. The identification of the onset of abnormal conditions from signal measurements taken in such evolving environments can be quite challenging, due to the difficulty of distinguishing the real cause of the signal variations. In this work, we present a method for fault detection in evolving environments that uses a Sparse Autoencoder-based Deep Neural Network (SAE-DNN) and a novel procedure that remarkably reduces the computational burden for setting the values of the hyperparameters. The method is applied to a synthetic case study and to a bearing vibration dataset. The results show that it is able to accurately detect faults in multi-component systems, outperforming other state-of-the-art methods.

Suggested Citation

  • Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2022. "A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:reensy:v:220:y:2022:i:c:s0951832021007511
    DOI: 10.1016/j.ress.2021.108278
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    References listed on IDEAS

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    4. Jiao, Jinyang & Zhao, Ming & Lin, Jing & Liang, Kaixuan, 2019. "Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 41-54.
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