Rolling-Bearing Remaining Useful Life Prediction Using Adaptive Fusion-VHI and Differential Gaussian Process Regression

Accurate remaining useful life (RUL) prediction of rolling bearings is important for condition-based maintenance. This study proposes an interpretable rolling RUL prediction framework integrating adaptive multidomain degradation representation, prognostic-stage localization, and differential Gaussian process regression (Diff-GPR). A 48-dimensional feature pool is extracted from six vibration domains, and bearing-specific degradation-sensitive subsets are selected using monotonicity, trendability, robustness, and redundancy criteria. The retained features are directionally aligned, percentile-normalized, equally fused, and smoothed to construct a fusion virtual health indicator (Fusion-VHI). A two-stage localization strategy identifies sustained degradation onset and the subsequent prediction-ready point, after which Diff-GPR models multiscale degradation increments and recursively updates failure time and RUL. The framework was evaluated on all 15 bearings from the Xi’an Jiaotong University–Changxing Sumyoung Technology (XJTU-SY) dataset and all 17 bearings from the 2012 Prognostics and Health Management (PHM2012)/PRONOSTIA dataset. Full final rolling prediction coverage was achieved, with mean absolute error/root-mean-square error (MAE/RMSE) values of 6.33/7.26 min on XJTU-SY and 6.03/6.98 min on PHM2012, and overall values of 6.17/7.11 min. Representative 99% predictive intervals achieved 100% prediction-interval coverage probability, while signed-error analysis identified 13 early and 19 late final predictions with an overall mean signed error of +1.77 min.