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Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Author

Listed:
  • Khoa Tran

    (AIWARE Limited Company, Da Nang City 550000, Vietnam)

  • Hai-Canh Vu

    (Laboratory for Applied and Industrial Mathematics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 70000, Vietnam
    Faculty of Mechanical-Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City 70000, Vietnam)

  • Lam Pham

    (AIT Austrian Institute of Technology GmbH, 1020 Vienna, Austria)

  • Nassim Boudaoud

    (Roberval Laboratory, Department of Mechanical Engineering, University of Technology of Compiègne, 60200 Compiègne, France)

  • Ho-Si-Hung Nguyen

    (Faculty of Electrical Engineering, University of Science and Technology—The University of Danang, Da Nang City 550000, Vietnam)

Abstract

Predictive maintenance (PdM) is one of the most powerful maintenance techniques based on the estimation of the remaining useful life (RUL) of machines. Accurately estimating the RUL is crucial to ensure the effectiveness of PdM. However, current methods have limitations in fully exploring condition monitoring data, particularly vibration signals, for RUL estimation. To address these challenges, this research presents a novel Robust Multi-Branch Deep Learning (Robust-MBDL) model. Robust-MBDL stands out by leveraging diverse data sources, including raw vibration signals, time–frequency representations, and multiple feature domains. To achieve this, it adopts a specialized three-branch architecture inspired by efficient network designs. The model seamlessly integrates information from these branches using an advanced attention-based Bi-LSTM network. Furthermore, recognizing the importance of data quality, Robust-MBDL incorporates an unsupervised LSTM-Autoencoder for noise reduction in raw vibration data. This comprehensive approach not only overcomes the limitations of existing methods but also leads to superior performance. Experimental evaluations on benchmark datasets such as XJTU-SY and PRONOSTIA showcase Robust-MBDL’s efficacy, particularly in rotating machine health prognostics. These results underscore its potential for real-world applications, heralding a new era in predictive maintenance practices.

Suggested Citation

  • Khoa Tran & Hai-Canh Vu & Lam Pham & Nassim Boudaoud & Ho-Si-Hung Nguyen, 2024. "Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines," Mathematics, MDPI, vol. 12(10), pages 1-25, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1569-:d:1396959
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    References listed on IDEAS

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    1. Yaqiong Lv & Pan Zheng & Jiabei Yuan & Xiaohua Cao, 2023. "A Predictive Maintenance Strategy for Multi-Component Systems Based on Components’ Remaining Useful Life Prediction," Mathematics, MDPI, vol. 11(18), pages 1-23, September.
    2. Wenbai Chen & Weizhao Chen & Huixiang Liu & Yiqun Wang & Chunli Bi & Yu Gu, 2022. "A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation," Mathematics, MDPI, vol. 10(7), pages 1-14, March.
    3. Stan Lipovetsky & Michael Conklin, 2001. "Analysis of regression in game theory approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(4), pages 319-330, October.
    4. Xiaopeng Xi & Donghua Zhou, 2022. "Prognostics of fractional degradation processes with state-dependent delay," Journal of Risk and Reliability, , vol. 236(1), pages 114-124, February.
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