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Selection of bearing health indicator by GRA for ANFIS-based forecasting of remaining useful life

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Abstract

Within the framework of Prognostic and Health Management, an attempt has been made to enhance prediction accuracy of the bearing remaining useful life (RUL) using the adaptive-network-based fuzzy inference system. First, the gray relational analysis has been employed to select the optimal bearing health indicators from 30 vibration features of time, frequency, and time–frequency domains. The selection was based on four criteria, namely correlation, monotonicity, average increase rate, and robustness. This allowed ranking the candidate features in terms of sensitivity to the bearing degradation evolution. The effectiveness of the proposed approach was evaluated on bearing run-to-failure test carried out on a wind turbine. The results of RUL prediction, based on the average of the present and the two previous values of the optimal feature, were very satisfactory, which can effectively optimize the maintenance costs of such equipment.

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Acknowledgements

This work was carried out at the University's LMS laboratory on May 8, 1945, Guelma, Algeria by the team (Coupe des Métaux) as part of a PRFU research project, Code: A11N01UN240120190001. The authors thank the Directorate General of Scientific Research and Technological Development (DGRSDT) of (MESRS) for the financial support.

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Authors and Affiliations

  1. Mechanics and Structures Laboratory, Université 8 Mai 1945 Guelma, BP 401, 24000, Guelma, Algeria

    Ikhlas Meddour, Ramdane Younes & Mohamed Athmane Yallese

  2. National Higher School of Technology, Dergana Bordj El Kiffan, Algiers, Algeria

    Ikhlas Meddour

  3. University of Science and Technology Houari Boumediene, Algiers, Algeria

    Salah Eddine Messekher

  4. Mechanical Engineering Department, Badji Mokhtar University, P.O. Box 12, 23000, Annaba, Algeria

    Ramdane Younes

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  1. Ikhlas Meddour
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  2. Salah Eddine Messekher
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  3. Ramdane Younes
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  4. Mohamed Athmane Yallese
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Correspondence to Ramdane Younes.

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Meddour, I., Messekher, S.E., Younes, R. et al. Selection of bearing health indicator by GRA for ANFIS-based forecasting of remaining useful life. J Braz. Soc. Mech. Sci. Eng. 43, 144 (2021). https://doi.org/10.1007/s40430-021-02878-w

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  • DOI: https://doi.org/10.1007/s40430-021-02878-w

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