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Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review

Author

Listed:
  • Vikas Singh

    (Shri G. S. Institute of Technology and Science)

  • Purushottam Gangsar

    (Shri G. S. Institute of Technology and Science)

  • Rajkumar Porwal

    (Shri G. S. Institute of Technology and Science)

  • A. Atulkar

    (Shri G. S. Institute of Technology and Science)

Abstract

The fault monitoring and diagnosis of industrial machineries are very significant in Industry 4.0 revolution but are often complicated and labour intensive. The application of artificial intelligence (AI) techniques have now been an important part of condition monitoring of the mechanical and electrical machines because of its fast computation, higher accuracy, and robustness in performance, reducing the dependence on experienced personnel with expert knowledge. This paper presents a review of applications of AI-based fault diagnosis techniques that have had demonstrated success when applied to various industrial machineries. The important literature published in the last twenty years (i.e., 2000 to 2020) have been reviewed and added. In this work, first, a brief of various AI techniques such as artificial neural networks (ANN), deep learning (DL), fuzzy logic (FL), and support vector machine (SVM) are added. The literature on AI-based diagnostics used for various industrial machines, such as induction motor, bearing, gear, and centrifugal pump, are added and discussed in detail. The observation, research gap, and new ideas have been discussed, followed by a conclusion.

Suggested Citation

  • Vikas Singh & Purushottam Gangsar & Rajkumar Porwal & A. Atulkar, 2023. "Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 931-960, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01861-5
    DOI: 10.1007/s10845-021-01861-5
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    References listed on IDEAS

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    1. Bing Han & Xiaohui Yang & Yafeng Ren & Wanggui Lan, 2019. "Comparisons of different deep learning-based methods on fault diagnosis for geared system," International Journal of Distributed Sensor Networks, , vol. 15(11), pages 15501477198, November.
    2. Xiaochuan Li & Faris Elasha & Suliman Shanbr & David Mba, 2019. "Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning," Energies, MDPI, vol. 12(14), pages 1-17, July.
    3. Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
    4. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    5. Kowalski, Czeslaw T & Orlowska-Kowalska, Teresa, 2003. "Neural networks application for induction motor faults diagnosis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 63(3), pages 435-448.
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