A diagnostics and prognostics framework for multi-component systems with wear interactions: Application to a gearbox-platform - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Pesquisa Operacional Année : 2022

A diagnostics and prognostics framework for multi-component systems with wear interactions: Application to a gearbox-platform

Résumé

We present a novel framework for diagnostics and prognostics for multi-component systems with wear interaction between components. The principal elements of this framework are: health-state indicator extraction using signal-processing; clustering of wear phases using a Gaussian mixture model; a stochastic multivariate wear model; and prediction of the remaining-useful-life of components using particle-filtering. These elements of the framework are illustrated and verified using an experimental platform that generates real data. Our diagnostics study shows that different clusters not only indicate the wear-state, but also the wear-rate of the components. Furthermore, our prognostics study shows that the wear-interaction between components has an significant impact in predicting the remaining-useful-life for components. Thus, we demonstrate, for prognostics and health management, the importance of modeling wear interactions in the prognostic process of multi-component systems.

Dates et versions

hal-03895696 , version 1 (13-12-2022)

Identifiants

Citer

Roy Assaf, Phuc Do Van, Phil Scarf. A diagnostics and prognostics framework for multi-component systems with wear interactions: Application to a gearbox-platform. Pesquisa Operacional, 2022, 42 (1), pp.1-30. ⟨10.1590/0101-7438.2022.042nspe1.00264770⟩. ⟨hal-03895696⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More