Contribution à la mise en oeuvre du Prognostic and Health Management (PHM) industriel


Ph.D. thesis


Asmaa MOTRANI
Université d'Oran 2 - Mohamed Ben Ahmed, 2022

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APA   Click to copy
MOTRANI, A. (2022). Contribution à la mise en oeuvre du Prognostic and Health Management (PHM) industriel (PhD thesis). Université d'Oran 2 - Mohamed Ben Ahmed.


Chicago/Turabian   Click to copy
MOTRANI, Asmaa. “Contribution à La Mise En Oeuvre Du Prognostic and Health Management (PHM) Industriel.” PhD thesis, Université d'Oran 2 - Mohamed Ben Ahmed, 2022.


MLA   Click to copy
MOTRANI, Asmaa. Contribution à La Mise En Oeuvre Du Prognostic and Health Management (PHM) Industriel. Université d'Oran 2 - Mohamed Ben Ahmed, 2022.


BibTeX   Click to copy

@phdthesis{motrani2022a,
  title = {Contribution à la mise en oeuvre du Prognostic and Health Management (PHM) industriel},
  year = {2022},
  institution = {Université d'Oran 2 - Mohamed Ben Ahmed},
  author = {MOTRANI, Asmaa}
}

Abstract

Operational safety and overall performance requirements involve improving the availability, reliability and safety of industrial systems while reducing their life cycle cost. One of the possible solutions is to evolve traditional maintenance, reactive or systematic, towards proactive, conditional, or predictive maintenance. The main specificity of predictive maintenance lies in the future prediction of failure commonly referred to as PHM (Prognostics and Health Management). The purpose of the industrial prognostic is to predict the future state of the monitored system and to give, more specifically, an estimate of its remaining useful life (RUL). Among the approaches used, data-based prognostic is the most promising when dealing with a multitude of heterogeneous data. The objective of this thesis is to present the implementation of a data-driven prognostic methodology, where the remaining useful life is determined by the combination of statistical methods and artificial intelligence. This proposed methodology is based on RVM-SBL (Relevance Vector Machine- Sparse Bayesien Learning) algorithm to build the predictive degradation model in the offline part, and RVM-SBI (Relevance Vector Machine- Sparse Bayesien Interpollation) algorithm to estimate a remaining useful life in the online part. The different steps of the proposed framework are described and implemented through two case studies. The results obtained were evaluated and validated and confirm the acceptability of the proposed methodology.

Keywords

Predictive Maintenance, Prognostic and Health Management, Data-driven approach, Relevance Vector Machine, Similarity Based Interpolation, Remaining Useful Life.




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