Physics-Informed Domain Generalization for Bearing Prognostics Under Unseen Operating Conditions

Physics-Informed Cross-Domain Fault Diagnosis for Rolling Bearings under Robustness and Interpretability: a Novel Feature Adaptation Framework

Fault diagnosis of rolling bearings is crucial for operational safety. However, the scarcity of labeled data and significant domain shifts are two key challenges. Existing studies neglect robustness to physical disturbances and the interpretability of diagnostic decisions. To address these issues, this paper proposes a physics-informed cross-domain fault diagnosis framework. First, based on bearing fault modulation mechanisms, multi-domain features are extracted. A physics-regularized feature selection strategy combining Sparse Group Lasso with stability selection and Random Forest is used to retain fault-relevant features. Recognizing that velocity variations are a major source of domain shifts, order analysis is introduced as a physics-prior alignment method. Deep CORAL and pseudo-label self-training are combined for unsupervised knowledge transfer. Finally, multi-level interpretability analysis is embedded to quantify domain shift, track adaptation geometry and explain final decisions. Two case studies demonstrate its robustness, trustworthiness and engineering applicability.

Linearly constrained multi-representation domain generalization for unseen-bearing RUL prediction

Accurate prediction of the remaining useful life (RUL) of rolling element bearings under target-bearing data scarcity remains a critical challenge in prognostics and health management (PHM). This paper proposes a multi-representation domain generalization framework for unseen-bearing RUL prediction. The framework uses a Bidirectional Multi-scale ConvLSTM architecture to capture temporal degradation dependencies and multi-granular spatial patterns from two aligned representations of the same vibration signal: the raw time-domain signal and its continuous wavelet transform (CWT) time-frequency representation. In addition, a lightweight linearly constrained temporal prior is integrated into the prediction layer to encourage a monotonically decreasing degradation trend, following the common damage-irreversibility assumption in bearing prognostics. The target bearing is excluded from training, validation, normalization-parameter estimation, and model selection, and is used only for final testing. Comprehensive experiments on two public benchmark datasets show that the proposed framework improves RUL prediction accuracy and provides competitive cross-bearing generalization.

A Physics-Informed Neural Network Framework Leveraging Deterministic Learning and Statistical Constraints for Bearing Fault Diagnosis

Recently, physics-informed learning-based intelligent bearing fault diagnosis methods have demonstrated great potential in improving diagnostic accuracy and physical consistency. Nevertheless, the practical deployment of conventional physics-informed neural networks typically relies on explicit partial differential equations (PDEs), which are generally difficult to acquire in real industrial scenarios. To address this limitation, a PDE-free physics-informed bearing fault diagnosis framework is proposed based on deterministic learning and statistical constraints. In the proposed framework, deterministic learning is adopted to extract nonlinear dynamic trajectories from multi-directional bearing vibration signals. The residuals between sample dynamic trajectories and class-specific standard trajectories are further converted into probabilistic statistical soft constraints. Accordingly, the system evolution mechanism embedded with physical dynamic characteristics is incorporated into the model training process, eliminating the need for explicit PDE constraints. In addition, a composite loss function is constructed by integrating the supervised classification loss with dynamic trajectory-based statistical constraint terms. This enables the network optimization process to be jointly guided by classification error and physical dynamic consistency. Finally, the effectiveness of the proposed method is validated through simulation and experimental studies. The results reveal that the average discernibility of dynamic trajectories reaches 0.3614, representing respective improvements of 122% and 68.6% over time-domain and frequency-domain signals. The proposed method achieves an effective balance among diagnostic accuracy, inference speed, and model complexity, with its average parameter volume reduced by approximately 93.9% relative to the comparative methods. These results demonstrate that the proposed method is applicable to bearing fault diagnosis scenarios where explicit PDE information is unavailable.

Remaining useful life prediction of electric vehicle drive system using physics-informed machine learning methods with uncertainty quantification

A reliable rolling bearing fault diagnosis method based on Titan

TitanDiag, a recently proposed architecture for long-context language modelling, tackles a similar challenge of maintaining performance across varying contexts by adapting this mechanism to fault diagnosis for rolling bearings.

Cross-Domain Bearing Fault Diagnosis Using Quantile-Based Domain Adaptation and Random Subspace Ensemble