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Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction

Author

Listed:
  • A. Mosallam

    (University of Franche-Comté/CNRS/ENSMM/UTBM)

  • K. Medjaher

    (University of Franche-Comté/CNRS/ENSMM/UTBM)

  • N. Zerhouni

    (University of Franche-Comté/CNRS/ENSMM/UTBM)

Abstract

Reliability of prognostics and health management systems relies upon accurate understanding of critical components’ degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of physical or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HIs) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline HI, to the online HI, using k-nearest neighbors classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian filter. The method is verified using battery and turbofan engine degradation simulation data acquired from NASA data repository. The results show the effectiveness of the method in predicting the RUL for both applications.

Suggested Citation

  • A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
  • Handle: RePEc:spr:joinma:v:27:y:2016:i:5:d:10.1007_s10845-014-0933-4
    DOI: 10.1007/s10845-014-0933-4
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    References listed on IDEAS

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    1. Li, Lin & Ni, Jun, 2009. "Short-term decision support system for maintenance task prioritization," International Journal of Production Economics, Elsevier, vol. 121(1), pages 195-202, September.
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    Cited by:

    1. Tian Wang & Meina Qiao & Mengyi Zhang & Yi Yang & Hichem Snoussi, 2020. "Data-driven prognostic method based on self-supervised learning approaches for fault detection," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1611-1619, October.
    2. Zhe Li & Yi Wang & Kesheng Wang, 2020. "A data-driven method based on deep belief networks for backlash error prediction in machining centers," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1693-1705, October.
    3. Hemir da Cunha Santiago & José Carlos da Silva Cavalcanti & Ricardo Bastos Cavalcante Prudêncio & Mohamed A. Mohamed & Leonie Asfora Sarubbo & Attilio Converti & Manoel Henrique da Nóbrega Marinho, 2023. "A Novel Remaining Useful Estimation Model to Assist Asset Renewal Decisions Applied to the Brazilian Electric Sector," Energies, MDPI, vol. 16(6), pages 1-24, March.
    4. Thirupathi Samala & Vijaya Kumar Manupati & Maria Leonilde R. Varela & Goran Putnik, 2021. "Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review," Future Internet, MDPI, vol. 13(3), pages 1-18, February.
    5. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
    6. Adolfo Crespo Marquez & Juan Francisco Gomez Fernandez & Pablo Martínez-Galán Fernández & Antonio Guillen Lopez, 2020. "Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models," Energies, MDPI, vol. 13(15), pages 1-19, July.
    7. Riku-Pekka Nikula & Konsta Karioja & Kauko Leiviskä & Esko Juuso, 2019. "Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1563-1579, April.
    8. Maroua Said & Khaoula ben Abdellafou & Okba Taouali, 2020. "Machine learning technique for data-driven fault detection of nonlinear processes," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 865-884, April.
    9. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    10. Qianhui Wu & Keqin Ding & Biqing Huang, 2020. "Approach for fault prognosis using recurrent neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1621-1633, October.
    11. Jie Yang & Shaowen Lu & Liangyong Wang, 2020. "Fused magnesia manufacturing process: a survey," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 327-350, February.
    12. Hua-Xi Zhou & Chang-Guang Zhou & Hu-Tian Feng, 2023. "An integrated lifetime prediction method for double-nut ball screws subject to preload loss failure mode," Journal of Risk and Reliability, , vol. 237(6), pages 1248-1258, December.
    13. Saideep Nannapaneni & Sankaran Mahadevan & Abhishek Dubey & Yung-Tsun Tina Lee, 2021. "Online monitoring and control of a cyber-physical manufacturing process under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1289-1304, June.
    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. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    16. Shan-Jen Cheng & Wen-Ken Li & Te-Jen Chang & Chang-Hung Hsu, 2021. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models," Energies, MDPI, vol. 14(18), pages 1-17, September.
    17. Gerardo Emanuel Granados & Loïc Lacroix & Kamal Medjaher, 2020. "Condition monitoring and prediction of solution quality during a copper electroplating process," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 285-300, February.
    18. Merainani, Boualem & Laddada, Sofiane & Bechhoefer, Eric & Chikh, Mohamed Abdessamed Ait & Benazzouz, Djamel, 2022. "An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples," Renewable Energy, Elsevier, vol. 182(C), pages 1141-1151.
    19. Mengrui Zhu & Yun Yang & Xiaobing Feng & Zhengchun Du & Jianguo Yang, 2023. "Robust modeling method for thermal error of CNC machine tools based on random forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2013-2026, April.
    20. Yu Mo & Qianhui Wu & Xiu Li & Biqing Huang, 2021. "Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1997-2006, October.
    21. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    22. Thi-Tinh Le & Seok-Ju Lee & Minh-Chau Dinh & Minwon Park, 2023. "Design of an Improved Remaining Useful Life Prediction Model Based on Vibration Signals of Wind Turbine Rotating Components," Energies, MDPI, vol. 17(1), pages 1-18, December.
    23. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    24. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
    25. Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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