IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2209-d847039.html
   My bibliography  Save this article

A Novel Method for Remaining Useful Life Prediction of Bearing Based on Spectrum Image Similarity Measures

Author

Listed:
  • Bo Wu

    (School of Transportation Engineering, Jiangsu Vocational Institute of Architectural Technology, Xuzhou 221116, China)

  • Bo Zhang

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China)

  • Wei Li

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Fan Jiang

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Accurately predicting the remaining useful life (RUL) of bearing by analyzing vibration signals is challenging and meaningful. To address this issue, a novel method based on spectrum image similarity is proposed in this paper. First, spectrum images for the whole lifecycle data of reference bearings are obtained by performing fast Fourier transformation (FFT). Second, the similarity is calculated between the current monitored data of operating bearing and run-to-failure images of reference bearings. Then, the weights of reference bearings are derived based on the similarity measures. Finally, the RUL of the operating bearing is estimated with the weighted average of the RULs of referenced bearings. The proposed method is demonstrated based on 2012 PHM Data Challenge Competition data, which shows its effectiveness and practicality.

Suggested Citation

  • Bo Wu & Bo Zhang & Wei Li & Fan Jiang, 2022. "A Novel Method for Remaining Useful Life Prediction of Bearing Based on Spectrum Image Similarity Measures," Mathematics, MDPI, vol. 10(13), pages 1-10, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2209-:d:847039
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2209/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/13/2209/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dejan G. Ćirić & Zoran H. Perić & Nikola J. Vučić & Miljan P. Miletić, 2023. "Analysis of Industrial Product Sound by Applying Image Similarity Measures," Mathematics, MDPI, vol. 11(3), pages 1-27, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    3. Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    4. Bellaera, R. & Bonifetto, R. & Di Maio, F. & Pedroni, N. & Savoldi, L. & Zanino, R. & Zio, E., 2020. "Integrated deterministic and probabilistic safety assessment of a superconducting magnet cryogenic cooling circuit for nuclear fusion applications," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    7. Yuanju Qu & Zengtao Hou, 2022. "Degradation principle of machines influenced by maintenance," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1521-1530, June.
    8. Lu, Ningyun & Huang, Shoujin & Li, Yang & Jiang, Bin & Kaynak, Okyay & Zio, Enrico, 2024. "Dynamic weight-based accelerated test modeling for fault degradation and lifetime analysis," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    9. Ahmad Farhat & Christophe Guyeux & Abdallah Makhoul & Ali Jaber & Rami Tawil & Abbas Hijazi, 2019. "Impacts of wireless sensor networks strategies and topologies on prognostics and health management," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2129-2155, June.
    10. Mengyao Gu & Youling Chen, 2019. "Two improvements of similarity-based residual life prediction methods," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 303-315, January.
    11. Abdenour Soualhi & Mourad Lamraoui & Bilal Elyousfi & Hubert Razik, 2022. "PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
    12. Malinowski, Simon & Chebel-Morello, Brigitte & Zerhouni, Noureddine, 2015. "Remaining useful life estimation based on discriminating shapelet extraction," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 279-288.
    13. Al-Dahidi, Sameer & Di Maio, Francesco & Baraldi, Piero & Zio, Enrico, 2016. "Remaining useful life estimation in heterogeneous fleets working under variable operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 109-124.
    14. Mengyao Gu & Youling Chen, 2018. "A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 987-998, October.
    15. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    16. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    17. Vega, Manuel A. & Hu, Zhen & Todd, Michael D., 2020. "Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    18. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
    19. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    20. Zhiguo Zeng & Francesco Di Maio & Enrico Zio & Rui Kang, 2017. "A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods," Journal of Risk and Reliability, , vol. 231(1), pages 36-52, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2209-:d:847039. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.