# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 3 Results and Discussion
### The Effect of Denoising on the Effect of Clustering Experiments  
In order to compare and analyze the impact of denoising on the test results, we counted the changes in the classification indicators of unsupervised machine learning and supervised machine learning, respectively. It can be seen from Table 8 that if unsupervised learning is used for binary classification experiments, the accuracy and recall rate of the data after denoising will be greatly improved and the time spent will be shortened compared with that before denoising. It proves that the data features after denoising are more obvious than those before denoising. After noise reduction, the redundant data that need to be processed by the algorithm are reduced, so the usage time is reduced. The silhouette coefficient before noise reduction has a slight increase compared with that after noise reduction, which proves that the clustering effect is better after noise removal. The F value in the evaluation index indicates that the results of the test sample can represent the true degree of the whole. Because the data before and after denoising are clustered with all the signal samples, no test samples are proposed for learning, so the F value before and after denoising has not changed. The entropy value reflects the reliability of the information. A small decrease in the entropy value after denoising indicates that the original signal tends to be stable after removing the messy noise signal. From the results obtained in these two tables, it can be seen that for the decision tree algorithm and the AdaBoost algorithm, the accuracy and recall rates are slightly improved. However, for the double SVM algorithm, the accuracy and recall rate did not increase significantly, which shows that for the supervised clustering algorithm, whether denoising has an impact on the clustering accuracy, but the impact is not significant. There is no significant difference in time consumption, indicating that noise does not affect the time complexity of supervised clustering algorithms. The same as the unsupervised clustering algorithm, the F value in the evaluation index indicates that the results of the test sample can represent the true degree of the whole. However, because the supervised algorithm \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & **Accuracy/\%** & **Time Cost/ms** & **Recall Rate** & **Silhouette Coefficient** & **F Value** & **Entropy Value** \\ \hline decision tree & 96.7 & 96.7 & 18 & 0.968 & 0.966 & 0.987 \\ Adaboost & 96.7 & 96.7 & 867 & 0.968 & 0.967 & 0.986 \\ double SVM & 96.1 & 95.8 & 1690 & 0.960 & 0.960 & 0.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Supervised clustering features of signals after denoising. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Accuracy/\%** & **Time Cost/ms** & **Recall Rate/\%** & **Silhouette Coefficient** & **F Value** & **Entropy Value** \\ \hline Before denoising & 77 & 3.5 & 71 & 0.834 & 0.96 & 0.242 \\ After denoising & 96 & 1.92 & 97.14 & 0.928 & 0.96 & 0.237 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Unsupervised clustering features of signals before and after denoising. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & **Accuracy/\%** & **Time Cost/ms** & **Recall Rate** & **Silhouette Coefficient** & **F Value** & **Entropy Value** \\ \hline decision tree & 93.3 & 93.3 & 20 & 0.933 & 0.933 & 0.987 \\ Adaboost & 93.3 & 93.3 & 899 & 0.933 & 0.933 & 0.986 \\ double SVM & 96.0 & 95.8 & 1690 & 0.960 & 0.960 & 0.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Supervised clustering features of signals before denoising. obtains the classification label in advance, and the denoised samples can better show the characteristics of their own category, the F value increases after denoising.