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This is a Quantitative Study study.

October 2, 2025

Remaining useful life prediction for rolling bearing based on a hybrid data-driven method integrating similarity analysis

Key Points

The hybrid method improves prediction accuracy for remaining useful life under severe conditions.
By extracting high-fidelity health indicators, it captures degradation trends effectively, suppressing noise.
A relevance vector machine generates sparse representations crucial for probabilistic remaining useful life estimation.
Validation on various datasets demonstrates superior robustness, especially in early degradation without sacrificing efficiency.

Abstract

Abstract Accurately predicting bearings’ remaining useful life (RUL) under severe operating conditions is crucial for predictive maintenance. This work proposes a hybrid data-driven method integrating similarity analysis (HDIS) to enhance interpretability, robustness, and probabilistic RUL estimation. First, deep variational networks are utilized to extract high-fidelity health indicators (HIs) from time–frequency features through unsupervised representation learning. Subsequently, a relevance vector machine processes these HIs to generate sparse representations (SRs), capturing degradation trends while suppressing noise. For RUL prediction, degradation knowledge is transferred from reference bearings using similarity analysis; finally, an empirical degradation model integrates target and reference SRs. This framework enables extrapolation to predefined failure thresholds, yielding high-accuracy probabilistic RUL predictions. Validation on laboratory and benchmark datasets demonstrates the HDIS’s superior robustness and prediction accuracy over single-driven methods, particularly in early degradation phases, while maintaining computational efficiency.

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