**4. Conclusions**

This paper studies a ML-based fault diagnosis method for combined fault of wind turbines. LPFEWT is proposed to extract time-frequency domain features from vibration signals. And a GWO hyperparameter tuned SVM is employed for fault diagnosis. The method is verified on a DFIG wind turbine drivetrain fault test rig in the laboratory. The experimental results show that LPFEWT can greatly improve the accuracy of fault diagnosis and it is superior to other feature extraction approaches. The effect of the number of LPFEWT Fourier spectrum segments on fault diagnosis results is explored and a reasonable strategy to choose the number of segments is given. SVM is proved to be superior in this classification problem.

Compared with the existing analysis methods for combined fault, this ML-based method is efficient. After training the ML model at low computation costs, it can quickly handle the data of wind turbines working at different speeds and easily identify the faults without human knowledge. The method can also be applied to fault diagnosis of other rotating machinery.

**Author Contributions:** Conceptualization, Y.X. and M.L.; methodology, Y.X. and M.L.; software, M.L.; validation, Y.X. and J.X.; resources, Y.X.; data curation, M.L. and W.Y.; writing—original draft preparation, M.L.; writing—review and editing, Y.X. and J.X.; supervision, Y.X.; project administration, Y.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China (51577008).

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