*3.3. LPFEWT with Different Number of Fourier Spectrum Segments*

To explore the effect of the number of LPFEWT Fourier spectrum segments on fault diagnosis results, the diagnosis was carried out with different number of Fourier spectrum segments, using energies of empirical modes as features, the results are shown in Table 2.



From Table 2, it can be seen that when the number of LPFEWT Fourier spectrum segments is small, although the testing set has good accuracy, the training set accuracy is slightly lower. When the number of LPFEWT Fourier spectrum segments is 5, 6, 7, 8, the diagnosis performance does not change. When the number of LPFEWT Fourier spectrum segments is 9, the accuracy of training set is improved a little, but the accuracy of testing set is reduced. Therefore, the number of LPFEWT Fourier spectrum segments should not be too small or too large, and there is a range of proper number of segments. It is suggested that the number of LPFEWT Fourier spectrum segments is set to 6 first, if the diagnosis results is not good enough, increase the number of segments one by one.

### *3.4. Effectiveness of the Proposed SVM Based Method*

In the proposed method, we choose SVM for classification because it has superiority when dealing with small datasets. Since the samples of wind turbines in faults are relatively few. So deep learning which needs a large dataset is not suitable. Considering the speed of prediction after training, k-nearest neighbors (k-NN) algorithm which computes the distances between the instance and all the training instances to make decisions is abandoned. We compared SVM with naive Bayes, decision trees, random forests and artificial neural networks (ANN), the results are shown in Table 3.

**Table 3.** Comparison Results of Different ML Classification Models.


From Table 3, we can see, for this classification problem, SVM has the best training performance and the accuracy of the training set is 94.1176%. The decision trees model has the lowest accuracy on training set with the highest accuracy as SVM model on testing set. All the models have good generalization ability. This show the feature selected is powerful. SVM has the best testing set accuracy and medium training set accuracy. Obviously, SVM is the best choice for this particular wind turbine fault diagnosis problem, which has good generalization ability even on a small dataset and easy to use (only has two hyperparameters need to tune).
