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Jiannan Dong

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Open access Aug 2026

Industrial Internet-Oriented Unsupervised Hydro-Turbine Bearing Fault Diagnosis via Prototype-Disentangled Conditional Wasserstein Domain Adaptation

With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce a challenging unsupervised cross-scenario diagnosis problem. Specifically, diagnostic models trained on labeled historical data may suffer severe performance degradation when deployed to unlabeled online data collected under different hydraulic conditions, rotational speeds, or operating conditions. Furthermore, existing domain adaptation methods, in their pursuit of distribution alignment, frequently overlook a critical bottleneck that limits generalization performance: inter-class entanglement. Specifically, under intense hydraulic background noise and cross-condition distribution shifts, features belonging to distinct fault types are highly susceptible to aliasing within the feature space. To overcome these issues, this paper proposes a Conditional Wasserstein Adversarial Network with Bi-level Prototype Disentanglement Regularization (CWAN-BPDR). First, a Conditional Wasserstein Adversarial Network (CWAN) is constructed by combining the smooth-gradient property of Wasserstein distance with conditional adversarial alignment, thereby achieving stable and fine-grained category-level domain adaptation. Furthermore, to alleviate the inter-class entanglement problem that may arise during cross-domain alignment, a Bi-level Prototype Disentanglement Regularization (BPDR) term is designed. By jointly implementing source–target prototype alignment and prototype–feature bidirectional alignment, BPDR explicitly suppresses inter-class confusion and enhances intra-class compactness and inter-class separability in the feature space. Experimental results on the JNU and NEFU datasets demonstrate that CWAN-BPDR achieves average diagnostic accuracies of 97.82% and 98.99%, respectively, while significantly mitigating label entanglement in challenging cross-operating-condition tasks. These results indicate that the proposed method can effectively transfer diagnostic knowledge acquired from labeled historical operating conditions to unlabeled online monitoring data. It can therefore serve as an offline-trained diagnostic module for Industrial Internet of Things-based condition-monitoring platforms in hydropower systems.

Xueyi Li, Binghao Hu, Jiannan Dong et al. · 0 citations
Jul 2026

MAML-S3M: Selective state space meta-learning for cross-condition few-shot bearing fault diagnosis

Deep learning achieves widespread success in fault diagnosis. However, its effectiveness is hindered in practical industrial environments due to complex operating conditions and data sparsity. This article proposes a novel model-agnostic meta-learning framework based on a selective state space model (MAML-S3M) to address the challenge of cross-condition few-shot bearing fault diagnosis. The framework introduces three core innovations. First, the continuously stacked selective state space module dynamically adjusts its receptive field, enabling precise feature extraction under varying conditions. Second, the channel attention mechanism is combined with the selective state space model to capture multi-scale features, thereby enhancing the feature extraction capability of the model. Third, the introduction of an explicit information discarding strategy during meta-task optimization refines the meta-learning process, thereby yielding optimal parameters. Extensive experiments on bearing datasets across different operating conditions demonstrate that the proposed MAML-S3M achieves superior diagnostic accuracy, with an average accuracy of 99.18% across six cross-condition tasks, outperforming state-of-the-art methods such as generalized model-agnostic meta-learning (GMAML) by at least 1.1%. The improvements are particularly helpful in scenarios with complex operating conditions and scarce samples, maintaining over 94% accuracy even in the challenging “10-way 1-shot” setting. We have made the paper’s results publicly available on GitHub. The link is as follows: https://github.com/12138250/MAML-S3M .

Siyu Liu, Nan Wang, Xueyi Li et al. · 0 citations

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