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

Khoa Tran, Hai-Canh Vu (), Lam Pham, Nassim Boudaoud and Ho-Si-Hung Nguyen
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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
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

Mathematics, 2024, vol. 12, issue 10, 1-25

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.

Keywords: remaining useful life; industrial prognostics; rotating machines; deep learning; multimodal neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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