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Intelligent Fault Detection of Rotating Machinery Using Long-Short-Term Memory (LSTM) Network

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Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems (ICETIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 573))

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Abstract

Artificial intelligence is one of the fastest-growing fields across the board. In every corner of the globe, researchers are attempting to harness its promise. Artificial intelligence’s capabilities began to be harnessed across all industries with the onset of the fourth industrial revolution. All smart industries follow the deployment of predictive maintenance with the assistance of AI. With the use of deep learning, a subset of artificial intelligence, this article describes a method for diagnosing defects in rotating machinery. The long-short-term memory framework, a class of recurrent neural network, is used to classify the faults of a rotating machine element. The experiment uses vibration data collected from rolling element bearings under various fault circumstances. The findings indicate that the LSTM network is a promising method for spotting faults in rotating machine parts such gears, rolling element bearings, shafts, rotors, and so on.

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Authors and Affiliations

  1. School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    A. Anwarsha & T. Narendiranath Babu

Authors
  1. A. Anwarsha
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  2. T. Narendiranath Babu
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Corresponding author

Correspondence to T. Narendiranath Babu .

Editor information

Editors and Affiliations

  1. Department of Business Analytics, Sunway University, Subang Jaya, Selangor, Malaysia

    Mohammed A. Al-Sharafi

  2. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  3. Al Buraimi University College, Al Buraimi, Oman

    Mohammed Naji Al-Kabi

  4. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

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Anwarsha, A., Narendiranath Babu, T. (2023). Intelligent Fault Detection of Rotating Machinery Using Long-Short-Term Memory (LSTM) Network. In: Al-Sharafi, M.A., Al-Emran, M., Al-Kabi, M.N., Shaalan, K. (eds) Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems . ICETIS 2022. Lecture Notes in Networks and Systems, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-031-20429-6_8

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