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A systematic review of data-driven approaches to fault diagnosis and early warning

Author

Listed:
  • Peng Jieyang

    (Tongji University
    Karlsruhe Institute of Technology)

  • Andreas Kimmig

    (Karlsruhe Institute of Technology)

  • Wang Dongkun

    (University of Macau)

  • Zhibin Niu

    (Tianjin University)

  • Fan Zhi

    (Fraunhofer)

  • Wang Jiahai

    (Tongji University)

  • Xiufeng Liu

    (Technical University of Denmark)

  • Jivka Ovtcharova

    (Karlsruhe Institute of Technology)

Abstract

As an important stage of life cycle management, machinery PHM (prognostics and health management), an emerging subject in mechanical engineering, has seen a huge amount of research. Here the authors present a comprehensive overview that details previous and current efforts in PHM from an industrial big data perspective. The authors first analyze the historical development of industrial big data and its distinction from big data of other domains and summarize the sources, types, and processing modes of industrial big data. Then, the authors provide an overview of common representation and fusion (data pre-processing) methods of industrial big data. Next, the authors comprehensively review common PHM methods in the data-driven context, focusing on the application of deep learning. Finally, two industrial cases from our previous studies are included in this paper to demonstrate how the PHM technique may facilitate the manufacturing industry. Furthermore, a visual bibliography is developed for displaying current results of PHM in an appropriate theme. The bibliography is open source at “ https://mango-hund.github.io/ ”. The authors believe that future research endeavors will require an understanding of this previous work, and our efforts in this paper will make it possible to customize and integrate PHM systems quickly for a variety of applications.

Suggested Citation

  • Peng Jieyang & Andreas Kimmig & Wang Dongkun & Zhibin Niu & Fan Zhi & Wang Jiahai & Xiufeng Liu & Jivka Ovtcharova, 2023. "A systematic review of data-driven approaches to fault diagnosis and early warning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3277-3304, December.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02020-0
    DOI: 10.1007/s10845-022-02020-0
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    References listed on IDEAS

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    1. KIM, Junyung & ZHAO, Xingang & SHAH, Asad Ullah Amin & KANG, Hyun Gook, 2021. "System risk quantification and decision making support using functional modeling and dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    4. Jia Luo & Jinying Huang & Hongmei Li, 2021. "A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 407-425, February.
    5. Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    6. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    7. Dimaio, F. & Scapinello, O. & Zio, E. & Ciarapica, C. & Cincotta, S. & Crivellari, A. & Decarli, L. & Larosa, L., 2021. "Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Li, Yanting & Jiang, Wenbo & Zhang, Guangyao & Shu, Lianjie, 2021. "Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data," Renewable Energy, Elsevier, vol. 171(C), pages 103-115.
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    Cited by:

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    2. Su, Zhiheng & Lian, Penglong & Shang, Penghui & Zhang, Jiyang & Xu, Hongbing & Zou, Jianxiao & Fan, Shicai, 2024. "Semi-supervised source-free domain adaptation method via diffusive label propagation for rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    3. Jia Tian & Xingqin Zhang & Shuangqing Zheng & Zhiyong Liu & Changshu Zhan, 2024. "Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems," Mathematics, MDPI, vol. 12(9), pages 1-25, April.
    4. Jiasheng Yan & Yang Sui & Tao Dai, 2025. "A Particle Swarm Optimization-Based Ensemble Broad Learning System for Intelligent Fault Diagnosis in Safety-Critical Energy Systems with High-Dimensional Small Samples," Mathematics, MDPI, vol. 13(5), pages 1-21, February.
    5. Sean Rooney & Emil Pitz & Kishore Pochiraju, 2025. "AutoML-driven diagnostics of the feeder motor in fused filament fabrication machines from direct current signals," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1999-2016, March.
    6. Chuan Li & Yifan Wu & Manjun Xiong & Shuai Yang & Yun Bai, 2025. "Self-supervised fusion of deep soft assignments for multi-view diagnosis of machine faults," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2493-2507, April.

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