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

Tian Wang (), Meina Qiao, Mengyi Zhang (), Yi Yang and Hichem Snoussi
Additional contact information
Tian Wang: Beihang University
Meina Qiao: Beihang University
Mengyi Zhang: Nanjing Tech University
Yi Yang: Henan Polytechnic University
Hichem Snoussi: University of Technology of Troyes

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 7, No 2, 1619 pages

Abstract: 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.

Keywords: Fault detection; Self-supervised; Kernel PCA; Prognostics and health management (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10845-018-1431-x

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