Supervised Contrastive Learning Enhanced Deep Residual Shrinkage Network for Dual Uncertainty-Aware Bearing RUL Prediction

In the context of industrial bearing prognostics, conventional deep learning methods have demonstrated remarkable prediction capabilities but typically provide only deterministic Remaining Useful Life (RUL) estimates without quantifying prediction uncertainty. This significant limitation hinders reliable decision-making in critical maintenance scenarios where understanding prediction confidence is essential. While statistical approaches attempt to model degradation uncertainty, they often struggle with the complex degradation patterns exhibited by modern industrial bearings. This paper presents UABP-BTNSCL, a novel framework integrating supervised contrastive learning with Bayesian deep learning to address these challenges.A key innovation is the unique integration of a Deep Residual Shrinkage Network (DRSN) enhanced Temporal Convolutional Network backbone with a dual uncertainty quantification mechanism that simultaneously captures aleatoric uncertainty through Negative Log-Likelihood optimization and epistemic uncertainty via Monte Carlo Dropout. A key innovation lies in our SupCon-RUL strategy, which discretizes the degradation progression into meaningful stages to structure the feature space, significantly enhancing uncertainty estimation reliability. Extensive validation on PHM2012 and XJTU-SY benchmarks demonstrates the framework’s effectiveness, achieving superior prediction accuracy with average RMSE values of 0.086 and 0.077 respectively, while providing well-calibrated uncertainty bounds (PICP: 0.97, MPIW: 0.40). Ablation studies confirm that each component—DRSN enhancement, SupCon-RUL feature structuring, and Bayesian uncertainty estimation—contributes synergistically to the framework’s overall performance in both point prediction accuracy and uncertainty quantification quality.