# A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves
## 5 Experiment Validation and Discussion
### Recognition Effects Comparison for Different Methods  
This section adopts different methods to conduct a comparative analysis. On the one hand, the feature extraction method has been replaced and, on the other hand, the model construction method has been replaced. However, the basic steps are consistent with the content in Section 4.3. The purpose of this is to compare the effectiveness of different feature extraction algorithms and learning models and ultimately demonstrate the superiority of the proposed method. Training and testing the recognition model based on the CWT and SST features is performed. Table 2 shows the recognition results for the different methods. The F1 value represents a comprehensive indicator of the recall rate and accuracy [22]. The larger the F1 value, the higher the accuracy of the method and the lower the miss rate. The average F1 value represents the average of all test results. The minimum F1 value represents the smallest value among the F1 values obtained after multiple tests. Therefore, a high average F1 value indicates that the overall recognition effect of the method is good, whereas a high minimum F1 value indicates that the single recognition effect of the method is reliable. From the table, the superiority of our algorithm is demonstrated; its precision and recall are better than those for CWT. Moreover, the parameter scale of the CWT algorithm is similar to that of the algorithm in this paper. Furthermore, under the same resources, the proposed algorithm can better reflect the changes in the iron core looseness degree than the features extracted using CWT. The detection accuracy for the algorithm from this paper has higher reliability in multiple tests. Table 2 also shows the scheme of using synchrosqueezed wavelet transform to extract image features when using other diagnostic methods. The method proposed in this article has the smallest parameter size and the best diagnostic performance. In contrast, LeNet's diagnostic results are slightly worse [23]. However, AlexNet [24], VggNet [25] and ResNet [26] not only have lager parameter sizes but also have a significant difference between the average F1 value and the minimum F1 value. This indicates that their diagnostic results have strong volatility and that there are extreme situations during multiple testing processes. Therefore, the reliability of these algorithms is relatively low and cannot always provide convincing diagnostic results. Figure 10 is a comparison diagram of the confusion matrices for the four algorithms. From the figure, it can be seen that LeNet and VggNet have some misjudments regarding extreme looseness. AlexNet has large diagnostic errors in both the total looseness and slight looseness modes. ResNet does not perform well in diagnosing extreme looseness and total looseness. Further comparison between Figures 9 and 10 shows that the diagnostic accuracies of LeNet, VggNet and ResNet are relatively low, whereas the diagnostic results for AlexNet are more extreme. Either all recognition is correct or there is a large misjudgment. Its stability is poor. The above comparison results indicate that the algorithm proposed in this paper has both diagnostic accuracy and stability for identifying the iron core looseness degree. From Figure 10, it can be concluded that most misjudgments occur in adjacent looseness modes. By analyzing the original data and time-frequency spectrum of the vibration signals, the main reasons for this phenomenon are identified as follows: (1) In adjacent modes, the vibration signals have partially similar features, which leads to misjudgments in the diagnostic model. (2) The four algorithms did not fully extract the features of the iron core looseness in the time-frequency figures.