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

arXiv: https://arxiv.org/abs/2510.17846 · HTML: https://mlsanity.com/paper/2510.17846/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Between accurate prediction and poor decision making: the AI/ML gap](https://mlsanity.com/paper/2310.02029/) (2023)

## Built on by (most cited first, 5 of 5)

- [Neuronal Attention Circuit (NAC) for Representation Learning](https://mlsanity.com/paper/2512.10282/) (2025)
- [Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics](https://mlsanity.com/paper/2512.08499/) (2025)
- [FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning](https://mlsanity.com/paper/2605.04421/) (2026)
- [Neuronal Stochastic Attention Circuit (NSAC) for Probabilistic Representation Learning](https://mlsanity.com/paper/2605.26061/) (2026)
- [A Novel Multimodal RUL Framework for Remaining Useful Life Estimation with Layer-wise Explanations](https://mlsanity.com/paper/2512.06708/) (2025)

## Abstract (arXiv)

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.