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Prognostics and Health Management: A Review on Data Driven Approaches

Author

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  • Kwok L. Tsui
  • Nan Chen
  • Qiang Zhou
  • Yizhen Hai
  • Wenbin Wang

Abstract

Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health. In recent years, PHM has emerged as an essential approach for achieving competitive advantages in the global market by improving reliability, maintainability, safety, and affordability. Concepts and components in PHM have been developed separately in many areas such as mechanical engineering, electrical engineering, and statistical science, under varied names. In this paper, we provide a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches. Real world examples have been provided to illustrate the implementation of PHM in practice.

Suggested Citation

  • Kwok L. Tsui & Nan Chen & Qiang Zhou & Yizhen Hai & Wenbin Wang, 2015. "Prognostics and Health Management: A Review on Data Driven Approaches," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-17, May.
    • Handle: RePEc:hin:jnlmpe:793161
    DOI: 10.1155/2015/793161
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    Cited by:

    1. Alan Turnbull & James Carroll & Alasdair McDonald, 2022. "A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling," Energies, MDPI, vol. 15(14), pages 1-13, July.
    2. Mukhopadhyay, Koushiki & Liu, Bin & Bedford, Tim & Finkelstein, Maxim, 2023. "Remaining lifetime of degrading systems continuously monitored by degrading sensors," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Jonghyuk Kim & Hyunwoo Hwangbo & Soyean Kim, 2018. "An empirical study on real-time data analytics for connected cars: Sensor-based applications for smart cars," International Journal of Distributed Sensor Networks, , vol. 14(1), pages 15501477187, January.
    4. 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.
    5. Hu, Changhua & Xing, Yuanxing & Du, Dangbo & Si, Xiaosheng & Zhang, Jianxun, 2023. "Remaining useful life estimation for two-phase nonlinear degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Liao, Guobo & Yin, Hongpeng & Chen, Min & Lin, Zheng, 2021. "Remaining useful life prediction for multi-phase deteriorating process based on Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    9. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    10. Ong, Samuel & Al-Othman, Amani & Tawalbeh, Muhammad, 2023. "Emerging technologies in prognostics for fuel cells including direct hydrocarbon fuel cells," Energy, Elsevier, vol. 277(C).
    11. Albara M. Mustafa & Abbas Barabadi, 2021. "Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks," Energies, MDPI, vol. 14(15), pages 1-15, July.
    12. Zhang, Jian-Xun & Du, Dang-Bo & Si, Xiao-Sheng & Hu, Chang-Hua & Zhang, Han-Wen, 2021. "Joint optimization of preventive maintenance and inventory management for standby systems with hybrid-deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Alan Turnbull & James Carroll, 2021. "Cost Benefit of Implementing Advanced Monitoring and Predictive Maintenance Strategies for Offshore Wind Farms," Energies, MDPI, vol. 14(16), pages 1-14, August.
    14. Farzam Farbiz & Mohd Salahuddin Habibullah & Brahim Hamadicharef & Tomasz Maszczyk & Saurabh Aggarwal, 2023. "Knowledge-embedded machine learning and its applications in smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2889-2906, October.
    15. Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.

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