*High-Speed Train Traction System Reliability Analysis DOI: http://dx.doi.org/10.5772/intechopen.111911*

The four algorithms were trained with the SVM model under the training set while the test set classification accuracy (12) was used as the optimization target, and the optimal parameters obtained are shown in **Table 3**, and the adaptation curves are shown in **Figure 10**. As can be seen from **Table 3** and **Figure 10**, the fitness values corresponding to the IGWO optimized SVM parameters reach their maximum values earlier than those of GWO, PSO and GA, and the test set fault identification rate obtained by IGWO is higher than that of GWO, PSO and GA, indicating that IGWO can obtain the optimal SVM parameters quickly and accurately in the search domain than GWO, PSO and GA. In terms of convergence effect, the PSO and GA optimization algorithms obtain optimal results around 66 and 45 generations, respectively, and converge more slowly than the GWO and IGWO optimization algorithms. Comparing the GWO optimization algorithm, we can see that the IGWO and GWO algorithms have faster convergence speed, but the GWO algorithm is easy to fall into local


### **Table 3.**

*Parameter optimization results and fault identification rate.*

optimum. In summary, the IGWO-SVM classification model outperforms the other three classification models both in terms of fault recognition rate, adaptation and convergence speed.

The results of the IGWO-SVM classification model, GWO-SVM classification model, PSO-SVM classification model and GA-SVM classification model for the identification of five types of faults and normal states of the traction motor are shown in **Figures 11**–**14**. The hollow circle *o* indicates the actual fault category, + indicates the predicted fault category, and if the two overlap, the prediction is accurate; if not, the prediction is wrong. In the comparison experimental results, the GA-SVM classification model identified 171 groups out of 180 test samples, with an overall recognition rate of 95%, but 1 sample of category 3 (stator interturn short circuit fault) were incorrectly classified into category 2(air gap eccentricity fault), with a false alarm rate of 3.33%; 8 samples of category 6 (Coupled faults) was incorrectly classified into category 2 (air gap eccentricity fault), with a false alarm rate of 26.67%; From the above analysis, it can be seen that although GA-SVM has a high fault recognition rate, it is difficult to meet the requirement of less than 10% false alarm rate of high-speed train traction system in terms of local fault diagnosis.

The PSO-SVM classification model identified 176 groups in 180 test samples, with an overall recognition rate of 97.2%, but 1 sample of category 4 (broken rotor bars fault) was incorrectly classified into category 6(compound faults), with a false alarm rate of 3.33%; 1 sample of category 5 (bearing fault) was incorrectly classified into category 3 (stator interturn short circuit fault), with a false alarm rate of 3.33%; 4 samples of category 6 (coupled faults) was incorrectly classified into category 2 (air gap eccentricity fault), with a false alarm rate of 10%. From the comparative analysis results, it can be seen that PSO-SVM has a higher overall recognition rate compared to

**Figure 11.** *Fault diagnosis result in IGWO-SVM.*

**Figure 12.** *Fault diagnosis result in GWO-SVM.*

GA-SVM, but it is difficult to meet the requirement of less than 10% false alarm rate for high-speed train traction system faults in local fault diagnosis, especially compound faults.

**Figure 14.** *Fault diagnosis result in GA-SVM.*

The GWO-SVM classification model identified 177 groups out of 180 test samples, with an overall recognition rate of 98.33%. However, 1 sample of category 6 (Coupled faults) was incorrectly classified into category 2(air gap eccentricity fault), with a false alarm rate of 10%. Compared with GA-SVM and PSO-SVM, GWO-SVM has a higher overall recognition rate and also meets the requirement of less than 10% false alarm rate for high-speed train traction systems but increases the probability of misclassification of fault samples.

The SVM classification model optimized by IGWO identified 178 groups out of 180 test samples, with an overall recognition rate of 98.89%. The model only had two samples incorrectly classified into category 2 (air gap eccentricity fault) in category 6 (coupled faults), with a false alarm rate of 6.67%. The classification model proposed in this paper not only meets the requirement of less than 10% false alarm rate of highspeed train traction systems but also avoids increasing the probability of misclassification of fault samples.
