These results establish PadéNet-enhanced UDA as an accurate, broadly applicable approach for robust bearing fault diagnosis under varying operating conditions, with a reduced parameter count suited to resource-constrained embedded platforms.
Abstract
Variations in mechanical load constitute a dominant source of domain shift in data-driven fault diagnosis of rotating machinery, causing models trained under one operating condition to degrade sharply when deployed under another. This work addresses the problem through unsupervised domain adaptation (UDA)—which transfers diagnostic knowledge from a labeled source condition to an unlabeled target condition by aligning their feature distributions—and introduces Padé Approximant Neural Networks (PadéNets) as compact yet highly expressive feature extractors. One-dimensional PadéNet encoders are embedded into three established adaptation frameworks—Deep CORAL, Domain-Adversarial Neural Networks (DANNs), and Conditional Domain-Adversarial Networks (CDANs)—to learn load-invariant representations without any labeled target data. On the Case Western Reserve University benchmark, where the models operate directly on raw time-domain vibration signals, replacing conventional convolutional encoders with PadéNets consistently improves cross-load diagnostic accuracy, reaching up to 99.28% average target-domain accuracy at a low parameter count. To assess generalization to a more demanding setting, the CDAN–PadéNet configuration is further evaluated on frequency-domain representations of the Paderborn University dataset, where domain shift arises from simultaneous variation of load torque and radial force on bearings with real accelerated-lifetime damage, attaining 99.84% average accuracy across six cross-condition transfer tasks while requiring fewer parameters than competing methods. These results establish PadéNet-enhanced UDA as an accurate, broadly applicable approach for robust bearing fault diagnosis under varying operating conditions, with a reduced parameter count suited to resource-constrained embedded platforms.
To address the issue that intelligent fault diagnosis models for rolling bearings are susceptible to the effects of load and speed variations, as well as shifts in data distribution, when applied across different operating conditions, this paper proposes a fault diagnosis domain generalization method that combines mean difference constraints with domain adversarial learning. Taking raw one-dimensional vibration signals as input, this method first employs a multiscale one-dimensional convolutional network to extract fault impact features and periodic features across different time scales; Subsequently, mean difference constraints between source domains are introduced in the feature space to reduce the offset in the centers of feature distributions across different source operating conditions; simultaneously, a domain-adversarial discriminator based on gradient inversion layers is constructed to weaken the operating condition discriminative information in the feature representations, thereby prompting the model to learn domain-invariant features that possess both fault class discriminability and operating condition insensitivity. Experiments were conducted using two rolling bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU) to establish a leave-one-out cross-condition diagnosis task. The results show that the proposed method achieves an average accuracy of 99.08% ± 0.19% on the CWRU dataset, and an average accuracy of 91.86% ± 0.34% on the PU dataset, both outperforming comparison methods such as SVM, 1D-CNN, ResNet1D, TCN, CORAL, MMD, and DANN. Furthermore, a Welch’s t-test based on summary statistics indicates that the improvement over the best baseline method is statistically significant. Ablation experiments and parameter sensitivity analysis further validate the effectiveness of multiscale feature extraction, mean difference constraints, and domain adversarial learning in enhancing the model’s generalization capability across different operating conditions.
Jiabing Zhou, Xiang Gu, Bo Zhang et al.· Advanced Engineering&Pre...· 0 citations
Fault diagnosis of train bearings is crucial for railway safety, yet models trained on a single operating condition often experience significant performance degradation when subjected to variations in speed or load. This paper proposes a new single-source domain generalization (SDG) model named adaptive mask flow adversarial network (AMFAN), aiming to enhance generalization capability by effective cross-domain simulation based on learnable perturbations in feature space. An adaptive mask mechanism is designed to determine domain-sensitive feature elements. And a feature perturbation strategy is conducted to the determined feature elements via a pre-trained flow model. Experiments on two train bearing datasets verify the superior performance over state-of-the-art methods. The results prove that the controlled feature expansion provides a viable and robust pathway for SDG, showing strong potential for real-world train bearing fault diagnosis when confronting unknown operating conditions.
Jun Wang, Bo Yu, He Ren et al.· ISA transactions· 0 citations
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.
chenghao yan, Dongsheng 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
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
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.
Na Lei, Zhuo Wang, Xue Wang et al.· Engineering Research Express· 0 citations
