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Critical components identification based on experience feedback data in the framework of PHM

Abstract : Preventive maintenance is recognized nowadays as a way of addressing adequately industrial systems or assets health management problem. To this end, approaches such as prognostics and health management (PHM) are being developed by researchers to support making predictive maintenance decisions by relaying on quantitative indicators such as remaining useful life (RUL); that is basically the projected time to failure of a given system. In general, an industrial system is composed of many components which failure may lead to the failure of the system; so that identification of such components which are referred to as critical components, constitute therefore an important stake. The process of identifying such components is based on many methods encountered in the literature among which experience feedback is drawing more and more attention of researchers because of, among other reasons, the fact that companies dispose nowadays of huge amount of functioning data of their systems. The aim of this paper is to develop a methodology based on experience feedback to identify critical components of a given industrial system. The proposed methodology will be applied to a real world case in broadcast industry to show its feasibility.
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Submitted on : Friday, April 26, 2019 - 11:34:25 AM
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  • HAL Id : hal-02111797, version 1
  • OATAO : 21999

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Houda Sarih, Ayeley Tchangani, Kamal Medjaher, Eric Péré. Critical components identification based on experience feedback data in the framework of PHM. 16th IFAC symposium on information control problems in manufacturing - INCOM 2018, Jun 2018, Bergame, Italy. pp.429-434. ⟨hal-02111797⟩

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