# Multi-Scale Convolutional Recurrent Neural Network for
## 4 Experiment and Result Analysis
### Experiment and Result  
The bearing data from the CWRU dataset were used [43]. Defects ranging from 0.007 to 0.021 inches (0.0178 to 0.0533 cm) in diameter were seen in the inner orbit, the rolling element (i.e., the ball), and the outer orbit. Vibration data were recorded for motor loads of 0-3 Horsepower (motor speed 1797 to 1720 RPM). The fault test bench is illustrated in Figure 7.  
The bearing data collected from the simulator had a total of 10 states depending on the type of defect. Each state had 1200 samples and a total of 10 states; therefore, there were 12,000 data samples. Gaussian random noise was added to the total data set, because the factory bearing signal had a lot of noise. However, in the case of CWRU data, it was determined that the noise from the field was not collected. Thus, Gaussian random noise was added to make the form of CWRU data similar to that of the data extracted from  
To evaluate the performance of the proposed model, we evaluated it using 1D-CNN, MS-CNN, CNN-LSTM, and MS-CRNN algorithms. Each experiment showed a result of 50 epoch and was repeated more than 10 times. All experiments used the most important accuracy mentioned in the evaluation indicator, and the F-1 score was used to compensate for the shortcomings in accuracy. For an accurate experiment, a total of three experiments were conducted. First, an experiment with the original CWRU data was conducted to evaluate the data set without additional noise. The results are shown in Table 5 and Figure 8.  
As a result of the experiment, it can be seen that the accuracy of all algorithms was over 99%. However, faster than other algorithms, MS-CRNN reached 100% accuracy. This would be unrealistic, because, in the case of CWRU data, most defects were well categorized because the data were extracted in a clean environment. As a result, it would \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Type** & **Fault Diameters** & **Class Num** \\ \hline Normal & - & 0 \\ Ball & 0.007 & 1 \\ Ball & 0.014 & 2 \\ Ball & 0.021 & 3 \\ Inner race & 0.007 & 4 \\ Inner race & 0.014 & 5 \\ Inner race & 0.021 & 6 \\ Outer race & 0.007 & 7 \\ Outer race & 0.014 & 8 \\ Outer race & 0.021 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of CWRU data. Figure 8: Plot of the experimental results obtained for the original CWRU data state. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **1D-CNN** & **MS-CNN** & **CNN-LSTM** & **MS-CRNN** \\ \hline Accuracy & 99.53\% & 99.66\% & 99.99\% & 100\% \\ F1 Score & 99.42\% & 99.59\% & 99.94\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the experiment in original CWRU data condition. be impractical to classify defects using the original CWRU data. Therefore, in this paper, an experiment was conducted by adding noise in order to create data of an environment similar to that of the field. Second, to check the performance of MS-CRNN, we evaluated it using 1D-CNN, MS-CNN, CNN-LSTM, and MS-CRNN algorithms in the presence of noise. Table 6 shows the results of the comparison. All algorithms had low accuracies when classifying defects in bearing data in noisy situations. However, it can be seen that the performance of the proposed algorithm was higher than those of other algorithms. Figure 9 shows a graph that visualizes the accuracy curves for each model. As is evident from the graph, the accuracy of the proposed model increased rapidly. Subsequently, in situations where the noise was removed using the DAE, the evaluation was conducted using 1D-CNN, MS-CNN, CNN-LSTM, and MS-CRNN. The experiment was conducted with the foregoing epoch. As listed in Table 7, the proposed model showed an average accuracy of more than 90%, and it can be seen that the accuracy of the entire algorithm increased when the problem was solved using the DAE. In addition, we see that the proposed model had a higher accuracy than the other models did.