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Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation

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Artificial Intelligence Techniques for a Scalable Energy Transition

Abstract

Scientific research in the area of fault prognosis is increasingly focused on estimating the Remaining Useful Life of equipment, since its knowledge is a key input to the scheduling of Condition-Based and Predictive Maintenance. Several research studies have been directed to developing methods for modeling the trend of health indicators for Remaining Useful Life estimation, this paper makes a review of these approaches. Fault diagnosis methods sensitive to the progressive evolution of degradation phenomena are presented and their usability for fault prognosis is discussed. Then, methods for modeling the trends of health indicators are analyzed to highlight the selection criteria of the modeling methods, according to the available information on the operating conditions of the systems, and on the degradation phenomenon. Finally, some reflections are made regarding the elements that prevent the large-scale use of prognostics in industry today, and on the integration of prognostics in risk assessment and management.

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Author information

Authors and Affiliations

  1. LIS, UMR 7020, Marseille, France

    Mohand Arab Djeziri

  2. LASA, Badji-Mokhtar Annaba University, Annaba, Algeria

    Samir Benmoussa

  3. Energy Department, Politecnico di Milano, Milan, Italy

    Enrico Zio

  4. Foundation Électricité de France (EDF), Centrale Supélec, Université Paris Saclay, Chatenay-Malabry, France

    Enrico Zio

Authors
  1. Mohand Arab Djeziri
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  2. Samir Benmoussa
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  3. Enrico Zio
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Corresponding author

Correspondence to Mohand Arab Djeziri .

Editor information

Editors and Affiliations

  1. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Djeziri, M.A., Benmoussa, S., Zio, E. (2020). Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation. In: Sayed-Mouchaweh, M. (eds) Artificial Intelligence Techniques for a Scalable Energy Transition. Springer, Cham. https://doi.org/10.1007/978-3-030-42726-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-42726-9_8

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  • Online ISBN: 978-3-030-42726-9

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