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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1431-x
    DOI: 10.1007/s10845-018-1431-x
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    References listed on IDEAS

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    1. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    2. Ridha Ziani & Ahmed Felkaoui & Rabah Zegadi, 2017. "Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 405-417, February.
    3. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
    4. Mario A. Saucedo-Espinosa & Hugo Jair Escalante & Arturo Berrones, 2017. "Detection of defective embedded bearings by sound analysis: a machine learning approach," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 489-500, February.
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    Citations

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    Cited by:

    1. Abdellatif Elmouatamid & Brian Fricke & Jian Sun & Philip W. T. Pong, 2023. "Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches," Energies, MDPI, vol. 16(12), pages 1-20, June.
    2. Youngju Kim & Hoyeop Lee & Chang Ouk Kim, 2023. "A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 529-540, February.
    3. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    4. Lu-jun Cui & Man-ying Sun & Yan-long Cao & Qi-jian Zhao & Wen-han Zeng & Shi-rui Guo, 2021. "A novel tolerance geometric method based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 799-821, March.
    5. Chia-Yen Lee & Chen-Fu Chien, 2022. "Pitfalls and protocols of data science in manufacturing practice," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1189-1207, June.
    6. Mohammed Majid Abdulrazzaq & Nehad T. A. Ramaha & Alaa Ali Hameed & Mohammad Salman & Dong Keon Yon & Norma Latif Fitriyani & Muhammad Syafrudin & Seung Won Lee, 2024. "Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts," Mathematics, MDPI, vol. 12(5), pages 1-42, March.
    7. Jinhai Chen & Wenyuan Zhang & Heng Wang, 2021. "Intelligent bearing structure and temperature field analysis based on finite element simulation for sustainable and green manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 745-756, March.

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