## 4 Results and Discussion
### Impact of SNR  
This section studies the influence of the signal-to-noise ratio on the performance of the crack location detection network. In this section, the performance of five algorithms for crack location detection at an SNR of \(-6\) dB to \(10\) dB is analyzed. The corresponding results are shown in Figure 11. The number of training samples is \(700\). It can be seen from the figure that when the SNR is \(10\) dB, that is, when the ratio of signal power to noise power is large, the average accuracy of the five algorithms is above \(98\%\), and the accuracy is almost unchanged. Among them, the average accuracy of the model proposed in this paper, support vector machine and random forest reach \(100\%\), with high accuracy. When the signal-to-noise ratio decreases from \(10\) dB to \(2\) dB, the accuracy of random forest, Xgboost and naive Bayesian algorithms will decrease significantly with the increase of noise power. However, the average accuracy of the model proposed in this paper and the support vector machines, which are always \(100\%\), do not decrease, and the detection effect is good. When the signal-to-noise ratio decreases from \(2\) dB to-\(6\) dB, the five algorithms decrease significantly with the increase of noise power. When the signal-to-noise ratio is \(-2\) dB and \(-4\) dB, that is, the noise power exceeds the signal power, the average accuracy of the model proposed in this paper is the highest, which is \(100\%\) and \(98.2\%\), respectively, while the accuracy of the other four machine learning algorithms is less than \(52\%\), and the detection effect is poor. When the SNR is \(-6\) dB, the average accuracy of the model proposed in this chapter is \(69\%\), and the classification effect is poor. However, the accuracy of the other four machine learning algorithms are lower than \(40\%\), which is lower than the model proposed in this paper. From the above analysis, the model has the highest accuracy under the signal-to-noise ratio of-\(6\) dB to \(10\) dB, and the anti-noise performance is stronger than the traditional machine learning algorithm.  
Figure 12 shows the confusion matrix of the crack location detection model established in this paper when the SNR is \(-6\) dB to \(-4\) dB. It can be seen from Figure 12a that when the signal-to-noise ratio is \(-6\) dB, \(41\%\) and \(57\%\) of the samples in the healthy rotor (Case 0) are detected as cracks on the left side of the double-disk rotor (Case 1) and cracks on the right side of the double-disk rotor (Case 2), respectively. This shows that when the noise power is high, the healthy rotor is easier to be confused with cracks on the double-disk rotor, resulting in wrong classification results. Among the samples of double disk rotor left crack (Case 1), \(13\%\) and \(21\%\) of the samples were misclassified as double disk rotor right crack (Case 2) and single disk rotor left crack (Case 3), respectively. Among the samples of the right side crack (Case 2) of the double disk rotor, \(38\%\) of the samples were misclassified as the left side crack (Case 3) of the single disk rotor, indicating that the cracks on the double-disk rotor are more easily confused with the cracks on the single-disk rotor. The  
Figure 11: Influence of SNR on accuracy.  
accuracy of the single disk rotor crack (Case 3 and Case 4) is 97% and 100%, respectively, while only 3% of the sample of Case 3 is misclassified as Case 2. From the above analysis, it can be seen that under the influence of noise, the real signal is easily misclassified as working conditions close to it in the physical location. Figure 12b shows that when the signal-to-noise ratio is \(-4\), only 3% of the healthy rotor (Case 0) are misclassified as the right side crack of double disk rotor (Case 2), while the accuracy of the other cases is 100%. Under the above two signal-to-noise ratios, the accuracy of the healthy rotor is the lowest. This shows that the increase of the noise power makes the signal of the healthy rotor easy to be misclassified as other working conditions.