Title: Data-driven prognostic framework for remaining useful life prediction

Authors: Asmaa Motrani; Rachid Noureddine

Addresses: University of Oran 2 Mohamed Ben Ahmed, Institute of Maintenance and Industrial Safety, Oran, Algeria ' University of Oran 2 Mohamed Ben Ahmed, Institute of Maintenance and Industrial Safety, Oran, Algeria

Abstract: Industrial prognostic, based on data resulting from a monitoring up stream, is considered as a crucial stage in making complex industrial systems more reliable. The purpose of the industrial prognostic is to predict the future state of the monitored system, and to give, more specifically, an estimation of its remaining useful lifetime (RUL). Among the used approaches, data-driven prognostic is the most promising when dealing with multitude heterogeneous data. The aim of this work is to present a data-driven prognostic framework implementation, where the RUL is determined through the association of statistical and artificial intelligence methods. This framework is based on the relevance vector machine (RVM) technique to build the predictive degradation model in the offline part, and on the similarity-based interpolation (SBI) technique for the prediction of the remaining useful life in the online part. The different steps of the proposed framework are described and implemented through a case study.

Keywords: prognostic and health management; PHM; data-driven prognostic; sparse Bayesian learning; SBL; relevance vector machine; RVM; sparse Bayesian interpolation; SBI; remaining useful life; RUL.

DOI: 10.1504/IJISE.2023.128666

International Journal of Industrial and Systems Engineering, 2023 Vol.43 No.2, pp.210 - 221

Received: 10 Feb 2020
Accepted: 08 Mar 2021
Published online: 01 Feb 2023 *

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