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Jiabing Zhou

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Open access Aug 2026

A Study on Domain Generalization Methods for Rolling Bearing Fault Diagnosis Based on Mean Differences and Domain Adversarial Learning

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. · 0 citations
Open access Aug 2026

A Method for Domain Generalization in Rolling Bearing Fault Diagnosis Based on Mamba and Causal Prototype Decoupling Constraints

To address the insufficient generalization capability of rolling bearing fault diagnosis models under complex operating conditions such as variable rotational speed, variable load, and variable radial force, this paper proposes a domain generalization fault diagnosis method based on Mamba feature extraction and causal generalization loss. First, the raw vibration signals are standardized and segmented using a sliding window strategy. Then, the selective state space model Mamba is employed to model long-range temporal dependencies and local dynamic variations in fault impact signals. Subsequently, from the perspective of causal invariance, generalization constraints are constructed by treating stable representations related to fault categories as causal features, while regarding amplitude variations, noise disturbances, and speed fluctuations induced by operating-condition changes as non-causal factors. A causal generalization loss consisting of class-conditional causal prototype consistency loss and feature decoupling regularization is designed to enhance the model’s adaptability to unseen operating conditions. Experiments are conducted on the Paderborn University (PU) and JNU datasets, where four operating conditions are regarded as four domains to construct cross-condition diagnosis tasks under a leave-one-condition-out evaluation protocol. The proposed method is compared with support vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), one-dimensional residual network (ResNet1D), temporal convolutional network (TCN), correlation alignment (CORAL), domain-adversarial neural network (DANN), maximum mean discrepancy (MMD), and vanilla Mamba. Experimental results show that the proposed method achieves an average accuracy of 93.56% on the PU dataset and 96.28% on the JNU dataset, outperforming all comparison methods and further demonstrating its effectiveness.

Jiabing Zhou, Xiang Gu, Bo Zhang et al. · 0 citations