Jul 2026· Measurement science and technology· Vol 37, pp. 316108· 0 citations· 31 references
Physics
TL;DR
Results indicate that DPR-PLO provides a feasible and robust solution for privacy-preserving bearing health management in new wind turbines, and adopts a two-stage pseudo-label refinement procedure to reduce noise propagation during self-training.
Abstract
Newly built wind farms often face three practical barriers for bearing fault diagnosis: limited fault samples, expensive labeling, and restricted access to historical source-domain data due to privacy and security requirements. This paper presents a source-free domain adaptation framework named dual perturbation robust pseudo-label optimization (DPR-PLO) for cross-domain wind turbine bearing diagnostics using only a pre-trained source model and unlabeled target data. DPR-PLO combines noise-aware hybrid perturbation learning with dynamic prototype alignment to improve robustness against condition-induced distribution shifts. Specifically, it applies time–frequency perturbations and feature-space adversarial perturbations to enhance target diversity, constructs discriminative target representations via dynamic clustering and prototype updating, and adopts a two-stage pseudo-label refinement procedure to reduce noise propagation during self-training. Experiments on Paderborn University Dataset, Jiangnan University Dataset, and a self-collected dataset demonstrate that DPR-PLO achieves 97.56% average accuracy across 18 cross-condition tasks, surpassing benchmark methods by 6.95% and yielding more reliable pseudo-labels. These results indicate that DPR-PLO provides a feasible and robust solution for privacy-preserving bearing health management in new wind turbines.
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.
Cheng-Hao Yan, Dong-Sheng Liu, Tong Wu et al.· Measurement science and tech...· 0 citations
Extensive experiments on multiple bearing fault datasets demonstrate that CSR-DGAN outperforms existing generative augmentation methods in terms of distribution similarity, cross-domain consistency, and downstream diagnostic performance, highlighting the effectiveness of the proposed problem-driven dual-domain generative framework for robust fault diagnosis under imbalanced conditions.
Lifang Chen, Zihan Ren, Lingjing Kong et al.· International Journal of Dat...· 0 citations
This work proposes DACL-IDA (Dynamic alignment and compactness learning for imbalanced domain adaptation), built on an alignment-scheduling principle rather than a new alignment loss: global adversarial alignment is delayed until classification warmup and black-box shift estimation (BBSE) have stabilized, and is kept restrained thereafter.
Xiangyu Peng, Yang Tao, Lei Hua et al.· Measurement science and tech...· 0 citations
Most domain-generalization methods for sensor fault diagnosis require multiple labeled source domains. When only one source domain is available, inter-domain variations cannot be directly observed, making it difficult to identify informative samples and avoid overfitting to source-specific features. To address these challenges, a dual-branch self-guided network (DBSGN) is proposed. In the guidance branch, energy discrepancy and local feature distance identify class-representative samples, while style consistency selects domain-representative samples. In the decision branch, multi-scale feature extractors learn complementary representations, and distribution-uncertainty-guided interleaved learning enables the fault classifier to exploit different feature scales. Two-stage contrastive learning improves cross-scale distribution consistency, intra-class compactness, and inter-class separability. Scale-blurring adversarial training further suppresses scale-specific information and promotes transferable representation learning without target-domain access during model development. Experiments on real-world sensor data from a nickel flash smelting system show that DBSGN achieves an average accuracy of 94.89% across twelve cross-process-domain diagnostic tasks, 3.44 percentage points higher than the best comparison method under the same protocol. These results support its effectiveness in the studied single-source domain-generalization setting.
T. Chu, Zhaiwen Wang, Dongnian Jiang· Measurement science and tech...· 0 citations
MAML is enhanced by incorporating a Sample Relationship Exploration module that learns intra-class similarity to improve class separability and replaces the fixed inner-loop update scheme in MAML with a trapezoidal gradient descent scheduler that adapts the number of inner-loop update steps across training.
Zhigang Chen, HaSitieer MaDetihan, Zhihao Zhang et al.· Engineering Research Express· 0 citations
An unsupervised contrastive learning framework named FDACL is proposed, providing an unsupervised diagnostic approach for railway bearings under unknown working conditions and outperforms state-of-the-art baselines on SDG transfer tasks.
Kai-Sheng Deng, Ping Qu· Italian National Conference...· 0 citations
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