The effective remaining useful life (RUL) prediction of rolling bearings could guarantee mechanical equipment reliability and stability. The hybrid physical and data-driven prognosis model (HPDM) is recently receiving increasing attention. However, HPDM approaches suffer from two significant challenges that limit their applicability to complex prognosis scenarios: (1) the reality gap between the simulation and measurement data and (2) the limited model generality to accommodate different working conditions and machines. From the perspective of leveraging physical model inference as ’teachers’ for the data-driven model, this article proposes a calibrated-based hybrid transfer learning framework to improve the data fidelity and model generality. Firstly, a 5-DOF dynamic model of rolling bearing is constructed. Comprehensively considering the crack and spall behaviors of degradation evolution, the physical model could provide various failure trajectories. Secondly, the particle filter-based calibration is proposed to retain the high fidelity of the physical simulation. Finally, a Physics-informed Bayes Deep Dual Network (PI-BDDN) is designed. The designed network fuses the physical calibrated simulation as augmented input space to learn representative prognosis features and makes the transfer learning process interpretable by combining the physical model parameters into adversarial learning to selectively identify the most informative knowledge for RUL prediction. The effectiveness of the proposed method is verified on two representative bearing datasets, and comparative results show the superiority of the proposed method on prediction accuracy and uncertainty quantification.

As mission-critical components in aircraft flap actuation systems, aerospace self-lubricating bearings play a pivotal role in ensuring operational safety during critical flight phases. Conventional machine learning approaches for remaining useful life (RUL) prediction suffer from limited time-series and nonlinear modelling capabilities. Especially individual models, built on a single algorithmic paradigm, struggle to address the complex, multi-dimensional challenges of RUL prediction. In order to solve the above challenge, this study proposes an innovative hybrid deep learning method integrating modernized temporal convolutional Networks (ModernTCN) with attention-enhanced Transformer (MTCAT) for RUL prediction. First, ModernTCN employs optimized dilated convolutions to capture multi-scale local temporal patterns efficiently. Second, an attention-enhanced Transformer leverages multi-head self-attention to model long-range dependencies and global degradation trends. Finally, the MTCAT is validated on the custom test rig, replicating real-world conditions, where it achieves a 30-50% reduction in root mean square error (RMSE) compared to baseline models. By autonomously learning hierarchical features from raw data, the MTCAT model minimizes manual feature engineering, demonstrating robustness and scalability for RUL prediction.

Aerospace self-lubricating bearings are critical components in aircraft transmission systems, where wearinduced degradation under high-load and dynamic conditions poses significant challenges to operational safety and system longevity. In recent years, deep-learning methods have shown promise in wear prediction by leveraging abundant monitoring data from sensor networks. However, these methods often struggle to detect early-stage degradation and rely on labor-intensive feature engineering, limiting their effectiveness in handling noisy, high-dimensional data. To overcome these issues, this article proposes an improved variational autoencoder (VAE) and graph attention network method for wear prediction based on acoustic emission (AE) signals. First, a clustering-guided contrastive VAE (CGC-VAE) model is proposed to process noisy, high-dimensional AE signals. The CGC-VAE employs K-means clustering to segment wear stages, combined with Gaussian mixture model (GMM) regularization and contrastive learning, to extract low-dimensional, discriminative latent features. Subsequently, a temporal graph attention network (T-GAT) is proposed to construct a dynamic graph based on temporal proximity and feature similarity, which can effectively model the spatiotemporal relationships of latent features. It employs graph attention mechanism and a long short-term memory (LSTM) layer for accurate wear prediction. Finally, experimental validation on aerospace self-lubricating bearing datasets, covering full lifecycle and partial wear scenarios, demonstrates the superior accuracy and adaptability of the proposed method.