Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps

Date

2020-09-09

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Wiley

Type

Article

Peer reviewed

Yes

Abstract

In this study, a three-step remaining service life (RSL) prediction method, which involves feature extraction, feature selection, and fusion and prognostics, is proposed for large-scale rotatingmachinery in the presence of scarce failure data. In the feature extraction step, eight time-domain degradation features are extracted from the faulty variables. A fitness function as a weighted linear combination of the monotonicity, robustness, correlation, and trendabilitymetrics is defined and used to evaluate the suitability of the features for RSL prediction. The selected features are merged using a canonical variate residuals-based method. In the prognostic step, graymodel is used in combinationwith empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected froman operational industrial centrifugal pump and a compressor.

Description

open access article

Keywords

prognostics, remaining useful life, rotating machinery, scarce failure data

Citation

Li, X., Mba, D., Okoroigwe, E., Lin, T. (2020) Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps. Quality and Reliability Engineering International, pp.1–13.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)