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

Assaf, R; Do, P; Scarf, PA

Abstract

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.

Citation

Assaf, R., Do, P., & Scarf, P. (2022). A diagnostics and prognostics framework for multi-component systems with wear interactions: application to a gearbox-platform. Pesquisa operacional (Impresso), 42, https://doi.org/10.1590/0101-7438.2022.042nspe1.00264770

• Journal Article Type: Article
• Publication Date: Jan 1, 2022
• Deposit Date: Jan 12, 2023
• Publicly Available Date: Jan 12, 2023
• Journal: Pesquisa Operacional
• Print ISSN: 0101-7438
• Volume: 42
• DOI: https://doi.org/10.1590/0101-7438.2022.042nspe1.00264770
• Publisher URL: https://doi.org/10.1590/0101-7438.2022.042nspe1.00264770