CARLE: A Hybrid Deep-Shallow Learning Framework for Robust and Explainable RUL Estimation of Rolling Element Bearings

• URL: http://arxiv.org/abs/2510.17846v1
• Date: Fri, 10 Oct 2025 21:43:26 GMT
• Title: CARLE: A Hybrid Deep-Shallow Learning Framework for Robust and Explainable RUL Estimation of Rolling Element Bearings
• Authors: Waleed Razzaq, Yun-Bo Zhao,
• Abstract summary: Remaining Useful Life (RUL) estimation predicts how long a component, such as a rolling element bearing, will operate before failure.<n>Many RUL methods exist but often lack generalizability and robustness under changing operating conditions.<n>This paper introduces CARLE, a hybrid AI framework that combines deep and shallow learning to address these challenges.
• Score: 2.312232949770907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prognostic Health Management (PHM) systems monitor and predict equipment health. A key task is Remaining Useful Life (RUL) estimation, which predicts how long a component, such as a rolling element bearing, will operate before failure. Many RUL methods exist but often lack generalizability and robustness under changing operating conditions. This paper introduces CARLE, a hybrid AI framework that combines deep and shallow learning to address these challenges. CARLE uses Res-CNN and Res-LSTM blocks with multi-head attention and residual connections to capture spatial and temporal degradation patterns, and a Random Forest Regressor (RFR) for stable, accurate RUL prediction. A compact preprocessing pipeline applies Gaussian filtering for noise reduction and Continuous Wavelet Transform (CWT) for time-frequency feature extraction. We evaluate CARLE on the XJTU-SY and PRONOSTIA bearing datasets. Ablation studies measure each component's contribution, while noise and cross-domain experiments test robustness and generalization. Comparative results show CARLE outperforms several state-of-the-art methods, especially under dynamic conditions. Finally, we analyze model interpretability with LIME and SHAP to assess transparency and trustworthiness.

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