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Remaining service life prediction based on gray model and empirical Bayesian with applications to compressors and pumps
Author(s) -
Li Xiaochuan,
Mba David,
Okoroigwe Edmund,
Lin Tianran
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2756
Subject(s) - prognostics , feature selection , robustness (evolution) , data mining , computer science , feature extraction , artificial intelligence , bayesian inference , bayesian probability , engineering , pattern recognition (psychology) , biochemistry , chemistry , gene
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 rotating machinery 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 trendability metrics 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, gray model is used in combination with empirical Bayesian algorithm for RSL prediction in the presence of scarce failure data. The proposed approach is validated on failure data collected from an operational industrial centrifugal pump and a compressor.