Bearing fault diagnosis under varying working conditions faces challenges, including lack of labeled data, distribution discrepancies, and resource constraints. To address these issues, we propose a progressive knowledge distillation framework that transfers knowledge from a complex teacher model, utilizing a Graph Convolutional Network (GCN) with Autoregressive moving average (ARMA) filters, to a compact and efficient student model. To mitigate distribution discrepancies and labeling uncertainty, we introduce Enhanced Local Maximum Mean Square Discrepancy (ELMMSD), which leverages mean and variance statistics in the Reproducing Kernel Hilbert Space (RKHS) and incorporates a priori probability distributions between labels. This approach increases the distance between clustering centers, bridges subdomain gaps, and enhances subdomain alignment reliability. Experimental results on benchmark datasets (CWRU and JNU) demonstrate that the proposed method achieves superior diagnostic accuracy while significantly reducing computational costs. Comprehensive ablation studies validate the effectiveness of each component, highlighting the robustness and adaptability of the approach across diverse working conditions.

Real-world bearing fault diagnosis faces challenges due to lack of access to labeled data and missing or incomplete samples for certain faulty classes, requiring generated data and methods to mitigate imbalanced class distributions and partial-set problems. To address these challenges, we propose a novel physics-informed deep learning framework using a physics-informed synthetic data generation method with an augmented weighted hybrid domain adaptation approach that combines adversarial training with a novel Multi-Kernel Maximum Mean Squared Discrepancy (MK-MMSD) metric. Unlike traditional Domain adaptation method, MKMMSD considers both the mean and variance of data distributions to effectively bridge the domain gap between synthetic and real unlabeled data. Furthermore, we propose a self-adaptive multi-weight block to address the partial-set problem and a novel re-balanced feature Mixup (RF-Mixup) technique to mitigate imbalanced class distributions. Experimental results on the CWRU and JNU datasets demonstrate the effectiveness of our approach in overcoming these real-world challenges in bearing fault diagnosis.

Bearing fault diagnosis in real-world applications has challenges such as insufficient labeled data, changing working conditions of the rotary machinery, and missing data due to multi-rate sampling of sensors. Despite the numerous applications of conventional deep learning (DL) and domain adaptation methods in bearing fault diagnosis, these methods face challenges. Domain adaptation techniques neglect alignment across subdomains with the same class, and DL techniques do not consider data relationships and interdependencies. To tackle these challenges, this paper introduces a novel semi-supervised method based on ARMA graph convolution, adversarial adaptation, and multi-layer multi-kernel local maximum mean discrepancy (MK-LMMD). Structural information of data is extracted using ARMA graph convolution, adversarial adaptation is employed to decrease structural distribution discrepancy in the domains, and MK-LMMD is used to align the classes. Additionally, ARMA graph convolution and MK-LMMD can aid in reducing distribution discrepancy caused by missing data and changing working conditions.

Abstract placeholder: This work proposes a Spatial Graph Convolutional Neural Network (SGCNN) combined with domain adaptation techniques to improve bearing fault diagnosis under varying operational loads and conditions, achieving high accuracy by effectively aligning feature distributions.

Abstract placeholder: This study presents a hybrid deep learning model combining CNN and Bi-LSTM with an attention mechanism to effectively predict earthquakes by learning spatial and temporal features from seismic data, showing promising results in forecasting seismic events.

Abstract placeholder: We introduce an unsupervised domain adaptation method for bearing fault diagnosis that is robust to noise and variable loads, using deep subdomain adaptation to align feature distributions without requiring labeled target data.

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