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Remaining useful life estimation in heterogeneous fleets working under variable operating conditions

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  • Al-Dahidi, Sameer
  • Di Maio, Francesco
  • Baraldi, Piero
  • Zio, Enrico

Abstract

The availability of condition monitoring data for large fleets of similar equipment motivates the development of data-driven prognostic approaches that capitalize on the information contained in such data to estimate equipment Remaining Useful Life (RUL). A main difficulty is that the fleet of equipment typically experiences different operating conditions, which influence both the condition monitoring data and the degradation processes that physically determine the RUL. We propose an approach for RUL estimation from heterogeneous fleet data based on three phases: firstly, the degradation levels (states) of an homogeneous discrete-time finite-state semi-markov model are identified by resorting to an unsupervised ensemble clustering approach. Then, the parameters of the discrete Weibull distributions describing the transitions among the states and their uncertainties are inferred by resorting to the Maximum Likelihood Estimation (MLE) method and to the Fisher Information Matrix (FIM), respectively. Finally, the inferred degradation model is used to estimate the RUL of fleet equipment by direct Monte Carlo (MC) simulation. The proposed approach is applied to two case studies regarding heterogeneous fleets of aluminium electrolytic capacitors and turbofan engines. Results show the effectiveness of the proposed approach in predicting the RUL and its superiority compared to a fuzzy similarity-based approach of literature.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:109-124
    DOI: 10.1016/j.ress.2016.07.019
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    References listed on IDEAS

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    1. Cadini, F. & Zio, E. & Avram, D., 2009. "Model-based Monte Carlo state estimation for condition-based component replacement," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 752-758.
    2. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
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    5. 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.
    6. Moghaddass, Ramin & Zuo, Ming J., 2012. "A parameter estimation method for a condition-monitored device under multi-state deterioration," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 94-103.
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    Cited by:

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    3. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
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    5. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    6. Sameer Al-Dahidi & Francesco Di Maio & Piero Baraldi & Enrico Zio, 2017. "A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets," Journal of Risk and Reliability, , vol. 231(4), pages 350-363, August.
    7. Jahani, Salman & Zhou, Shiyu & Veeramani, Dharmaraj, 2021. "Stochastic prognostics under multiple time-varying environmental factors," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Belkacem, Lobna & Simeu-Abazi, Zineb & Dhouibi, Hedi & Gascard, Eric & Messaoud, Hassani, 2017. "Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 98-109.
    9. Dourado, Arinan & Viana, Felipe A.C., 2021. "Early life failures and services of industrial asset fleets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).

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