Target Label-Free Diagnosis of Rotor Bolted Joints via a Hybrid Physical-Data-Driven Partial Domain Adaptation Framework

Fault Classification in Robotic Arm with Ensemble Based Model-Agnostic Meta-Learning

To maintain high efficiency and reduce operational downtime in industrial manufacturing, effective Predictive Maintenance (PdM) for robotic manipulators is essential. Although combining Model-Agnostic Meta-Learning (MAML) with digital twin technology offers a solid basis for quickly identifying faults, conventional methods often face challenges regarding parameter sensitivity and generalizing to new domains. To mitigate these issues, we introduce an ensemble-based metalearning framework that combines MAML with majority voting and operational grouping. This methodology improves generalization, stabilizes performance across diverse conditions, and strengthens few-shot learning capabilities. We validated the framework using a synthetic vibration dataset generated via a digital twin to simulate various robotic arm faults. Our findings demonstrate that this method achieves 93.8% accuracy and 93.1% precision in the ten-shot regime, outperforming the MAML baseline by 11.1%, across a broad range of mechanical faults, showing strength in cross-domain few-shot (CDFS) scenarios. Comparisons with established frameworks - including Reptile, Protonet, and ANIL, confirm the effectiveness of our model. By employing ensemble learning, we attain greater robustness and classification accuracy, establishing the method as a viable solution for industrial PdM. Furthermore, the integration of digital twins bridges the gap between simulation and real-world deployment, reducing data dependency and enabling effective fault classification even in dynamic environments with limited labeled data.

Hybrid Loss-Driven Source-Free Domain Adaptation with Post-hoc Explainability for Bearing Fault Diagnosis

A novel and robust Source-Free Domain Adaptation (SFDA) framework equipped with post-hoc explainability for bearing fault diagnosis is proposed, and a post-hoc visualization mechanism is incorporated to explain the model's decision-making process, enhancing the transparency and credibility of the diagnosis for practical industrial deployment.

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.

A small sample transfer diagnosis method driven by a high-precision dynamic model for rolling bearings

Although intelligent diagnosis methods based on deep learning have achieved significant theoretical advancements, the application in real-world industrial scenarios remains challenging. The scarcity of fault samples in real-world environments hinders the effective training of deep learning models, thereby compromising their diagnostic accuracy and generalization ability. To address this issue, a small sample transfer diagnosis method driven by a high-precision dynamic model for rolling bearings is proposed this paper. Firstly, a high-precision dynamic model of rolling bearing with defects is constructed to simulate vibration signals under various fault types and severities, generating a large-scale and diverse library of simulated fault samples. Then, an improved Transformer-based deep transfer learning network is developed to extract features from both simulated samples and measured small samples at local and global scales, and perform multi-layer deep domain adaptation to minimize the distribution discrepancy between the simulated and measured data, facilitating accurate fault diagnosis under small sample conditions. Finally, experimental verification on two datasets with different tasks demonstrated that the proposed method not only effectively diagnoses rolling bearing faults but also exhibits excellent generalization ability on small sample datasets.

Fault diagnosis of rolling bearings via CEPADA: a partial adversarial domain adaptation method based on contrastive error perception

In cross-condition bearing fault diagnosis, the target domain often contains only a subset of the fault classes available in the source domain. This asymmetric label-space setting makes conventional domain alignment prone to negative transfer, because source-private and unreliable samples may still participate in adaptation. To address this problem, this paper proposes a contrastive error-perception-based partial adversarial domain adaptation method, named CEPADA. Instead of relying only on class-level target predictions, CEPADA introduces an error-perception mechanism to jointly evaluate class-level transfer reliability and sample-level uncertainty. Samples with high error risk are assigned lower influence during adversarial alignment, while more reliable shared-class samples are emphasized. Meanwhile, contrastive learning is embedded into the adaptation process to make features of the same fault class more compact and features of different classes more separable. A pseudo-label refinement strategy is also used to provide additional target-domain guidance under the unlabeled setting. Experiments on the case western reserve university and JNU bearing datasets show that CEPADA achieves average accuracies of 93.17% and 86.02%, respectively, with improvements of 1.47 and 1.52 percentage points over the best-performing compared unsupervised baseline in our experiments.

Global-to-Local Domain Adaptation with Neighbor-Aware Propagation for Cross-Domain Fault Diagnosis of Rotating Machinery