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Data-driven prognostic method based on self-supervised learning approaches for fault detection

Abstract : As a part of prognostics and health management (PHM), fault detection has been used in many fields to improve the reliability of the system and reduce the manufacturing costs. Due to the complexity of the system and the richness of the sensors, fault detection still faces some challenges. In this paper, we propose a data-driven method in a self-supervised manner, which is different from previous prognostic methods. In our algorithm, we first extract feature indices of each batch and concatenate them into one feature vector. Then the principal components are extracted by Kernel PCA. Finally, the fault is detected by the reconstruction error in the feature space. Samples with high reconstruction error are identified as faulty. To demonstrate the effectiveness of the proposed algorithm, we evaluate our algorithm on a benchmark dataset for fault detection, and the results show that our algorithm outperforms other fault detection methods.
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https://hal-utt.archives-ouvertes.fr/hal-03320646
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:32:18 AM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

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Tian Wang, Meina Qiao, Mengyi Zhang, Yi Yang, Hichem Snoussi. Data-driven prognostic method based on self-supervised learning approaches for fault detection. Journal of Intelligent Manufacturing, Springer Verlag (Germany), 2020, 31 (7), pp.1611-1619. ⟨10.1007/s10845-018-1431-x⟩. ⟨hal-03320646⟩

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