## 4 Experimental Results
### Classifying Compound Faults and Fault Degradation Levels  
Further, we conducted experiments to evaluate the ability of our proposed method under complex conditions. This was diagnosing bearing faults and their degradation level corresponding to three different crack sizes (3, 6, and 12 mm) (e.g., inner and outer raceway fault (BCIO)--3 mm) under variable rotational speed. Dataset 3 was used in this experiment.  
The results in Table 5 show that under noiseless conditions, the proposed method achieves an average accuracy of 98.21% for complex working environments. Under the presence of noise with positive SNR values, a few samples of the bearing conditions are misclassified, which deteriorates the overall prediction accuracy of the proposed model. However, the achieved accuracy is declined slightly to 96.08% when SNR is 10 dB and to 95.36% when SNR value is 5 dB. The worst accuracy is  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{
\begin{tabular}{c} **SNR** \\ **(dB)** \\ \end{tabular} } & \multicolumn{8}{c}{**Class Wise Accuracy (\%)**} & \multicolumn{2}{c}{**Average**} \\ \cline{3-11}  & & **BCI** & **BCO** & **BCR** & **BCIO** & **BCIR** & **BCOR** & **BCIOR** & **BNC** & **Accuracy** \\ \hline \multirow{3}{*}{**Dataset 1**} & 10 & 100.00 & 100.00 & 100.00 & 100.00 & 92.86 & 100.00 & 100.00 & 87.50 & 97.55 \\  & 5 & 85.71 & 92.31 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 91.67 & 96.21 \\  & 0 & 83.33 & 77.78 & 100.00 & 100.00 & 100.00 & 72.73 & 91.67 & 93.33 & 89.85 \\ \hline \multirow{3}{*}{**Dataset 2**} & 10 & 100.00 & 100.00 & 100.00 & 91.82 & 94.12 & 100.00 & 100.00 & 100.00 & 98.24 \\  & 5 & 80.00 & 100.00 & 100.00 & 80.00 & 100.00 & 92.31 & 100.00 & 100.00 & 94.03 \\ \cline{1-1}  & 0 & 90.91 & 88.24 &93.33% when SNR value is 0, showing that the proposed model provides high diagnostic accuracy and stability under complex working environments.  
Figure 9 shows the detailed FD accuracy of individual classes in no noise and various SNR conditions (22 classes: seven types of single and combined bearing faults, three degradation levels for each fault type and normal condition). It can be observed that in a complex scenario, misclassifications can occur in different classes because each fault class depends on various constraint conditions. It is hard to predetermine working conditions to focus on traditional methods such as fault frequency range. Instead, EfficientNet, which can classify up to 1000 classes when it is trained with a large enough dataset like ImageNet, is appropriate to classify each bearing fault class automatically in complex working environments. Figure 10 illustrates the confusion matrix of the fault classification results for Dataset 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SNR (dB)** & **Average Accuracy (\%)** \\ \cline{2-3}  & No noise & 98.21 \\
**Dataset 3** & 10 & 96.08 \\  & 5 & 95.36 \\  & 0 & 93.33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average accuracy of the proposed method in diagnosing compound faults and different degradation levels.  
Figure 9: Bearing faults diagnosis accuracy for compound faults and different degradation levels.