Xiaochuan Li

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.