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Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition

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Abstract

Bearing is one of the most sensitive components widely used in rotary machines and main cause for unexpected breakdown in rotating machinery. Bearing failure can lead to a lengthy downtime of the machine. Accurately predicting the damage trend of bearing is essential for planning maintenance, avoiding machine shutdowns and improving systems reliability. To reduce the maintenance cost of machine downtime, it is desirable to perform fault prognostics to enable predictive health management for bearings. This paper proposes a new data-driven approach for bearing prognostics based on wavelet packets decomposition and bidirectional long short-term memory, for preprocessing and tracking degradation process to estimate the remaining useful life. The proposed approach has two steps. The first step is to detect bearing’s degradation process by learning the historical data and the second step is to predict the remaining useful life with the aid of a degradation model. The proposed approach is validated by bearing’s life data obtained from a run-to-failure experiment. Results show that the proposed approach can detect the bearing degradation process successfully and predict the remaining useful life.

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Availability of data and materials

I, Dr Tarak Benkedjouh, corresponding author of the paper “Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition,” submitted for publication to the International Journal of Advanced Manufacturing Technology, declare that all the data used in this work are provided by the University of Cincinnati for Bearing dataset, IMS, NASA Ames Prognostics Data Repository, Rexnord Technical Services (2007), U.S.A, through their website [28].

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Acknowledgements

This study was completed in the Mechanical Structures laboratory of the Polytechnic School of Algeria by the contribution of all authors, and with financial support from the research division of the polytechnic school. The authors would like to thank the University of Cincinnati for bearing dataset, IMS, NASA Ames Prognostics Data Repository, Rexnord Technical Services (2007), U.S.A, for providing free and open access to the bearing data sheet from their website [28] .

Funding

I, Dr Tarak Benkedjouh, corresponding author of the paper “Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition,” submitted for publication to the International Journal of Advanced Manufacturing Technology, declare that all funding institutions are cited.

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Authors and Affiliations

  1. Ecole Militaire Polytechnique, Mechanical Structures Laboratory, Bordj Elbahri, 16046, Algiers, Algeria

    Houssem Habbouche & Tarak Benkedjouh

  2. FEMTO-ST Institute, UMR CNRS 6174- UFC / ENSMM / UTBM, Automatic Control and Micro-Mechatronic Systems Department, 24, rue Alain Savary, 25000, Besançon, France

    Noureddine Zerhouni

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  1. Houssem Habbouche
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  3. Noureddine Zerhouni
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Contributions

All the authors conceived and designed the study. Houssem Habbouche conducted the analysis of experimental data, isolation, extraction, selection of signals, statistical analysis, and preparation of manuscript. Tarak Benkedjouh started the preprocessing of the acceleration signals used in the study and the preparation of the manuscript. Noureddine Zerhouni contributed to signal analysis and manuscript revisions. All the authors approved the final version of the manuscript and agree to be held accountable for the content therein.

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Correspondence to Tarak Benkedjouh.

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I, Dr Tarak BBenkedjouh, corresponding author of the paper “Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition,” submitted for publication to the International Journal of Advanced Manufacturing Technology, certify that we have no potential conflict of interest for the mentioned article and that we respected the ethical rules and good scientific practices.

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I, Dr Tarak Benkedjouh, corresponding author of the paper “Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition,” submitted for publication to the International Journal of Advanced Manufacturing Technology, declare the consent of all the co-authors to participate in the aforementioned research paper.

Consent to publish

I, Dr Tarak Benkedjouh, corresponding author of the paper “Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition,” submitted for publication to the International Journal of Advanced Manufacturing Technology, declare the consent of all the co-authors to publish the aforementioned research paper in the International Journal of Advanced Manufacturing Technology.

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Habbouche, H., Benkedjouh, T. & Zerhouni, N. Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition. Int J Adv Manuf Technol 114, 145–157 (2021). https://doi.org/10.1007/s00170-021-06814-z

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