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The paper applies meta-learning, test-time adaptation, and prototype-based domain alignment to fault diagnosis on asynchronous motors, using a finite-element digital twin as a source of labeled simulation data. Its core claim is that a model can transfer knowledge from simulation to a physical asset with only a few labeled real samples. Readers should treat the results as validated on one motor type and a limited set of speeds, so generalization to other industrial assets remains an open question.
Bi-directional digital twin prototype anchoring with multi-periodicity learning for few-shot fault diagnosis
arXiv:2603.07054v2 Announce Type: replace Abstract: Intelligent fault diagnosis (IFD) typically requires abundant labeled data, which are difficult to obtain from physical assets in practical industrial applications. Digital twins (DTs) provide a promising alternative by generating labeled simulation data, while most existing DT-assisted diagnosis methods rely on domain adaptation and still require considerable unlabeled target-domain data. To enable diagnostic knowledge transfer when only a few labeled target samples are available, this paper proposes a bi-directional DT prototype anchoring framework with multi-periodicity learning for few-shot fault diagnosis. The model is first meta-trained in the DT virtual space to acquire transferable diagnostic knowledge and is then adapted to the physical asset through test-time adaptation. To improve cross-domain adaptation under extremely limited target data, a bidirectional twin-domain prototype anchoring strategy is developed to mutually constrain the source- and target-domain class representations, while covariance-guided augmentation is introduced to improve the robustness of target prototype estimation. In addition, a multi-periodicity feature learning module is designed to capture the intrinsic periodic characteristics of motor current signals. A finite-element-based DT of an asynchronous motor is constructed for experimental validation under three rotational speeds and multiple few-shot settings. The experimental results show that the proposed method outperforms representative comparison methods, while the ablation studies verify the effectiveness of the components. These results demonstrate the potential of the proposed framework for DT-to-asset diagnostic knowledge transfer under severely limited target-domain data.
Abstract:Intelligent fault diagnosis (IFD) typically requires abundant labeled data, which are difficult to obtain from physical assets in practical industrial applications. Digital twins (DTs) provide a promising alternative by generating labeled simulation data, while most existing DT-assisted diagnosis methods rely on domain adaptation and still require considerable unlabeled target-domain data. To enable diagnostic knowledge transfer when only a few labeled target samples are available, this paper proposes a bi-directional DT prototype anchoring framework with multi-periodicity learning for few-shot fault diagnosis. The model is first meta-trained in the DT virtual space to acquire transferable diagnostic knowledge and is then adapted to the physical asset through test-time adaptation. To improve cross-domain adaptation under extremely limited target data, a bidirectional twin-domain prototype anchoring strategy is developed to mutually constrain the source- and target-domain class representations, while covariance-guided augmentation is introduced to improve the robustness of target prototype estimation. In addition, a multi-periodicity feature learning module is designed to capture the intrinsic periodic characteristics of motor current signals. A finite-element-based DT of an asynchronous motor is constructed for experimental validation under three rotational speeds and multiple few-shot settings. The experimental results show that the proposed method outperforms representative comparison methods, while the ablation studies verify the effectiveness of the components. These results demonstrate the potential of the proposed framework for DT-to-asset diagnostic knowledge transfer under severely limited target-domain data.
Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
arXiv:2603.07054 [cs.AI]
(or arXiv:2603.07054v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2603.07054 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/JIOT.2026.3748376 Focus to learn more DOI(s) linking to related resources
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