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A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets

Author

Listed:
  • Sameer Al-Dahidi

    (Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan])

  • Francesco Di Maio

    (Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan])

  • Piero Baraldi

    (Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan])

  • Enrico Zio

    (SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec - Ecole Centrale Paris - Ecole Supérieure d'Electricité - SUPELEC (FRANCE) - CentraleSupélec - EDF R&D - EDF R&D - EDF - EDF, LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec, Dipartimento di Energia [Milano] - POLIMI - Politecnico di Milano [Milan])

Abstract

In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity–based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity–based models.

Suggested Citation

  • Sameer Al-Dahidi & Francesco Di Maio & Piero Baraldi & Enrico Zio, 2017. "A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets," Post-Print hal-01652222, HAL.
    • Handle: RePEc:hal:journl:hal-01652222
    DOI: 10.1177/1748006X17693519
    Note: View the original document on HAL open archive server: https://hal.science/hal-01652222
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    References listed on IDEAS

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    1. John J. McCall, 1965. "Maintenance Policies for Stochastically Failing Equipment: A Survey," Management Science, INFORMS, vol. 11(5), pages 493-524, March.
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    Cited by:

    1. Sameer Al-Dahidi & Piero Baraldi & Enrico Zio & Lorenzo Montelatici, 2021. "Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    2. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    3. Sameer Al-Dahidi & Manoharan Madhiarasan & Loiy Al-Ghussain & Ahmad M. Abubaker & Adnan Darwish Ahmad & Mohammad Alrbai & Mohammadreza Aghaei & Hussein Alahmer & Ali Alahmer & Piero Baraldi & Enrico Z, 2024. "Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework," Energies, MDPI, vol. 17(16), pages 1-38, August.
    4. Matteo Vagnoli & Francesco Di Maio & Enrico Zio, 2018. "Ensembles of climate change models for risk assessment of nuclear power plants," Journal of Risk and Reliability, , vol. 232(2), pages 185-200, April.
    5. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. Ma, Zhonghai & Liao, Haitao & Gao, Jianhang & Nie, Songlin & Geng, Yugang, 2023. "Physics-Informed Machine Learning for Degradation Modeling of an Electro-Hydrostatic Actuator System," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

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