CARLE: a hybrid deep-shallow learning framework for robust and explainable RUL estimation of rolling element bearings

Prognostic health management (PHM) systems have extensive applications in industry for monitoring and predicting the health status of equipment. Remaining Useful Life (RUL) estimation stands out as one important part of a PHM system that predicts the remaining operational lifespan of mechanical systems or their components, such as rolling element bearings, which account for a high proportion of machinery failures. Although many methods for RUL estimation have been developed, there are some challenges in terms of generalizability and robustness under dynamic operating conditions. This paper introduces the CARLE AI framework, which integrates advanced deep learning architectures with shallow machine learning technique to overcome these limitations. CARLE integrates Res-CNN and Res-LSTM blocks with multi-head attention and residual connections to capture spatial and temporal degradation trends coupled with Random Forest Regression (RFR) for robust and accurate predictions. We further propose a compact feature extraction framework that implements Gaussian filtering for efficient noise reduction and Continuous Wavelet Transform (CWT) for time–frequency feature extraction. We assessed the effectiveness of the proposed framework via the XJTU-SY and PRONOSTIA bearing datasets. Ablation experiments were conducted to assess the contribution of each component within CARLE, whereas noise experiments evaluated its resilience to noise. Cross-domain validation experiments were performed to examine the model’s generalizability across multiple domains. Additionally, comparative analyses with several state-of-the-art methods under dynamic operating conditions demonstrated that CARLE outperformed competing approaches, particularly in terms of generalizability to unseen scenarios. Furthermore, we discuss the reliability and trustworthiness of this framework via multiple state-of-the-art explainable AI (XAI) techniques, i.e., LIME and SHAP.