## 4 Results and Discussion  
### Evaluation of the Proposed Method  
Figure 8 shows the results of the accuracy and standard deviation between the crack location detection in this paper and the other four common machine algorithms in crack location detection. The above data are obtained by 10 tests on the same model. The signal-to-noise ratio is 0, that is, the noise and signal power are equal. CNN represents the convolutional neural network-based method proposed in this paper. SVM, RF, XG and NB represent support vector machine, random forest, Xgboost and naive Bayesian algorithms, respectively. It can be seen from the figure that the average accuracy of the model, support vector machine, random forest, Xgboost and naive Bayesian algorithm in crack location detection are 100%, 54.5%, 49.5%, 62.5% and 51.5%, respectively. The accuracy of this model is the highest. In addition, it can be found that in the 10 tests, the accuracy of the proposed method is 100%, and the standard deviation is almost 0. However, the accuracy of the other machine learning algorithms are not the same in the 10 tests, and there is a certain standard deviation. Figure 9 shows the confusion matrix of the crack location detection in this paper, whose horizontal coordinates represent the detection label, and the ordinates represent the real label. The value in the matrix represents the proportion of the number of samples detected as a row label by a signal actually belonging to a column label. The closer the value on the diagonal is to one, the better the effect of the detection model is. It can be seen from the figure that when the signal-to-noise ratio is equal to zero0, the diagonals of the confusion matrix are all one, indicating that the fault detection model can accurately classify healthy rotors and crack faults at four different locations. Figure 10 shows the visualization process of the model in crack location detection. The results shown in the figure are the distribution of the output of each residual layer in two-dimensional space after t-SNE dimensionality reduction. As can be seen from the figure, with the increase in the number of residual layers, the distinguishability of the five fault signals becomes stronger and stronger, and the indistinguishable signals in the low-dimensional space gradually become distinguishable in the high-dimensional space. This shows that it is important to increase the depth of the convolution network. After the first residual layer and the second residual layer, the characteristic distribution of the five working conditions is not obvious. The characteristics of the same working condition gather in different regions on the two-dimensional plane, and the aggregation degree is not close. This shows that the separability and robustness of each working condition are impotent. After the third residual layer, there is no cross between the characteristics of the healthy rotor condition (Case 0) and the two crack conditions on the single disk rotor (Case 3 and Case 4). The characteristics of the training set and the test set are basically clustered in the same area, which shows that the fault detection model has strong ability to distinguish the above three faults. However, it can be observed that in Case 1 and Case 2,  
Figure 8: Comparison of different algorithms in crack location detection. the sample characteristics of the two conditions overlap, which shows that the model has difficulty distinguishing the above two conditions. After the fourth residual layer, the sample characteristics of the five fault conditions are clustered in the same area and far from each other. This shows that the classification accuracy of the model is high. In addition, the sample characteristics of the training and test sets overlap almost entirely under various operating conditions, indicating that the generalization ability of the model is strong. Figure 10: Visualization of crack location detection process: (**a**) residual layer \(1\); (**b**) residual layer \(2\); (**c**) residual layer \(3\); (**d**) residual layer \(4\).