**5. Conclusions**

Fault diagnosis of wind turbines plays an important role in improving the reliability of wind turbines. However, the operating conditions of wind turbines vary randomly, and data on different operating conditions are not easily available.

In this paper, the wind turbine transmission platform data is supplemented by the generation of data for unknown operating conditions, which in turn improves the classification accuracy. The proposed TL-CVAE-GAN model combines the better performance of CVAE-GAN in generating samples with the idea of domain adaptive migration. It achieves the generation of unknown samples for wind turbine transmission platforms in different conditions and solves the classification problem of variable conditions data. Work conditions are input to the model as conditions, and the generation of data in different work conditions between similar classes is achieved by domain migration. The known data are used to train CVAE-GAN1. In CVAE-GAN, the known working conditions are fed into the encoder as conditional information to obtain the intermediate key information for the removal of the working conditions. The intermediate key information and the unknown conditions are fed together into the generator to generate the same class of data for the unknown conditions. The generation can be improved by confronting the generator with the discriminator.

The MMD between the known and unknown conditions is then calculated. The MMD is added to the loss of CVAE-GAN2, which is an unknown generator, to achieve the generator's domain migration. The problem of data imbalance for wind turbine gearboxes is solved by generating missing data for unknown working conditions via CVAE-GAN2. The raw data and generated data are fed into the classifier to train the model for classification.

The results show that the proposed model, TL-CVAE-GAN, effectively generates data for unknown working conditions. After the generated data of unknown operating conditions were added to the training set as a supplement, the test accuracy of the trained classifier was improved by 21.3%, effectively improving the fault diagnosis accuracy undersample imbalance. The model can better solve the problem of fault diagnosis of wind turbines with variable operating conditions.

**Author Contributions:** Writing—original draft, X.L.; Writing—review & editing, H.M. and Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

