Key Takeaways & Executive Findings
- •• • The proposed method achieves superior diagnostic accuracy under fault sample scarcity compared to classical approaches, as validated on a wind turbine planetary gearbox fault diagnosis test rig, directly addressing the industrial bottleneck where fault data are scarce due to automatic shutdowns. • • A high-fidelity lumped-parameter dynamic model generates pseudo-fault data that supplements the training set, mitigating the imbalance between abundant healthy data and scarce fault data without relying on oversampling or undersampling techniques that risk overfitting or information loss. • • The integration of convolutional block attention modules and local maximum mean discrepancy aligns pseudo and real fault data distributions at the fault-category level, reducing domain shift and enhancing feature transferability, which is critical for deploying models across different operating conditions. • • The Kolmogorov-Arnold network module improves the model's ability to learn complex data relationships, enabling robust classification of different fault types; however, the framework currently addresses only known fault types, and unknown fault identification remains an open challenge requiring open-set domain generalization.
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Abstract
Fault diagnosis of wind turbine planetary gearboxes is severely constrained by the scarcity of high-quality fault data, as gearboxes operate predominantly in healthy states and automatic shutdowns prevent fault progression. This paper proposes a data-model jointly driven diagnosis method to address low diagnostic accuracy under limited fault samples. A high-fidelity lumped-parameter dynamic model of the planetary gearbox is constructed to generate pseudo-fault data, supplementing the training set. A domain-shared residual network feature extractor incorporating convolutional block attention modules extracts key physical features from both pseudo and measured data. Local maximum mean discrepancy aligns feature distributions at the fault-category level between pseudo and real fault data. A Kolmogorov-Arnold network module enhances the model's capacity to learn complex data relationships, enabling classification and identification of different fault types. Validation on a wind turbine planetary gearbox fault diagnosis test rig demonstrates that the proposed method achieves superior diagnostic performance under fault sample scarcity compared to classical methods. The framework offers an effective solution for known fault types, though identification of unknown and atypical faults remains a challenge for future work via open-set domain generalization.
1. Introduction
Deep learning-based fault diagnosis for wind turbine planetary gearboxes has achieved high accuracy when trained on large datasets. In practice, however, gearboxes operate predominantly in healthy states, and fault occurrences trigger automatic shutdowns, resulting in a severe scarcity of fault data. This imbalance causes models to bias predictions toward healthy classes, undermining diagnostic reliability. Existing remedies at the data level—oversampling, undersampling, and generative adversarial networks—suffer from overfitting, information loss, or mode collapse, while algorithm-level solutions such as cost-sensitive loss functions struggle when labeled fault samples are extremely limited, leading to unreliable decision boundaries and poor generalization.
To overcome these limitations, this study introduces a data-model jointly driven diagnosis framework. A high-fidelity lumped-parameter dynamic model of the planetary gearbox is established to generate pseudo-fault data, enriching the training set without the drawbacks of synthetic oversampling. A domain-shared residual network with convolutional block attention modules extracts physical features from both pseudo and measured data, while local maximum mean discrepancy aligns their distributions at the fault-category level. A Kolmogorov-Arnold network module further enhances the learning of complex data relationships. Validation on a dedicated test rig confirms that the proposed method outperforms classical approaches under fault sample scarcity, providing a practical pathway for reliable diagnosis in wind turbine applications.
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ZENG Qingtao, TANG Guihua, ZHANG Xuan, CHENG Jijie, MA Ping (2026). Data-Model Jointly Driven Fault Diagnosis for Wind Turbine Planetary Gearboxes. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9731
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Frequently Asked Questions
What specific failure mechanisms in planetary gearboxes are addressed by the proposed method, and how does it improve diagnostic accuracy under fault sample scarcity?
The method targets sun gear faults that alter the time-varying meshing stiffness between the sun and planetary gears. By generating pseudo-fault data from a high-fidelity dynamic model and aligning feature distributions via local maximum mean discrepancy, it achieves superior diagnostic accuracy compared to classical methods, as validated on a wind turbine planetary gearbox test rig, even when fault samples are scarce.
How does the cost of implementing this data-model jointly driven approach compare to traditional data augmentation techniques such as GANs or oversampling?
The approach avoids the computational expense and instability of GAN training and the overfitting risks of oversampling. The lumped-parameter dynamic model is computationally efficient, and the domain adaptation network adds moderate complexity. While a direct cost comparison is not provided, the method reduces reliance on large fault datasets, potentially lowering long-term data collection and labeling costs in industrial deployments.
What are the scalability bottlenecks when deploying this diagnostic framework across different wind turbine models or operating conditions?
The framework requires accurate structural and operational parameters to build the dynamic model for each gearbox type. Variations in gearbox design or operating conditions may necessitate model recalibration. The domain adaptation component mitigates distribution shifts, but unknown fault types remain a challenge, limiting scalability to known fault scenarios without further open-set domain generalization.
How does the Kolmogorov-Arnold network module enhance classification performance compared to standard neural network architectures?
The Kolmogorov-Arnold network improves the model's ability to learn complex, nonlinear relationships in the data, which is crucial for distinguishing subtle differences between fault types. This leads to higher classification accuracy under scarce fault samples, as demonstrated by the experimental results showing superior performance over classical methods.
What are the limitations of the current framework regarding unknown or atypical fault identification, and what future work is proposed?
The framework currently provides effective solutions only for known fault types. In real industrial settings, unknown and atypical faults pose a significant challenge. Future research should integrate open-set domain generalization techniques to enable identification and classification of unknown fault types, thereby enhancing the model's practicality and robustness for intelligent fault diagnosis of industrial equipment.
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