## 4 Experimental Analysis and Verification
### Test III  
To further verify the cross-working conditions fault diagnosis ability of the proposed method, experimental verification was conducted on the bearing data of a drum motor for coal mine transportation collected underground in Xuzhou. The drum motor for coal mine transportation and audio collector are shown in Figure 9. The data used in test III were underground audio signals. There were four health statuses, namely bearing outer ring damage, rolling element damage, inner ring damage, and the normal state. We collected data under the working conditions of 500 r/min, 750 r/min, and 1000 r/min. The specific information of the bearing dataset for the drum motor is shown in Table 5.  
There were three cross-working condition fault diagnosis tasks in test III, namely I \(\rightarrow\) J, I \(\rightarrow\) K, and J \(\rightarrow\) K. We compared the different methods of DBN, S-Alexnet, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, ML, JDA, TCA-DBN, and DSAN with the proposed method. The detailed fault diagnosis results are presented in Table 6 and Figure 10. \begin{table}
\begin{tabular}{c c c} \hline
**Working Conditions** & **Rotational Speeds** & **Health Status** \\ \hline I & 500 r/min & Normal state \\ J & 750 r/min & Inner ring fault \\ K & 1000 r/min & Rolling element fault \\ Outer ring fault \\ \hline \end{tabular}
\end{table} 
Table 5: Dataset of the drum motor bearings. Figure 9: Drum motor for the coal mine transportation (the collected data on the bearings of the underground drum motor was used for the cross-working condition fault diagnosis). (**a**) Drum motor; (**b**) Audio acquisition device. As shown in Table 6 and Figure 10, the average accuracy of the DBN, S-Alexnet, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, ML, JDA, TCA-DBN, DSAN, and ITCA-DBN were 68.472%, 67.917%, 66.528%, 70.764%, 63.611%, 54.583%, 69.653%, 80.555%, 86.389%, and 91.944%, respectively. Due to the low signal-to-noise ratio of the data in this experiment, the accuracy of the cross-working condition fault diagnosis was lower than that of test I. The average accuracy of ITCA-DBN was the highest, and the effect was better than that of the other methods. Compared to TCA-DBN and DSAN, the average accuracy of ITCA-DBN increased by 11.389% and 5.555%, respectively. Based on the above comparative analysis, it can be clearly seen that the proposed ITCA and SRPLC K-means contributed to diagnosing faults under variable working conditions. To demonstrate that the fault diagnosis using the proposed method required less time, the time required for different methods to achieve fault diagnosis was calculated. The time required for different different methods is presented in Table 7 and Figure 11. We started timing from processing the raw data and stopped timing when we obtained the output diagnostic results for the test set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \(\mathbf{I\to J}\) & \(\mathbf{I\to K}\) & \(\mathbf{J\to K}\) &
\begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} \\ \hline DBN & 68.125 & 66.25 & 71.042 & 68.472 \\ S-Alexnet & 70.625 & 66.042 & 67.083 & 67.917 \\ IG-CWT-CNN & 67.292 & 66.875 & 65.417 & 66.528 \\ TF-MDA & 71.042 & 72.083 & 69.167 & 70.764 \\ ICPW-HPF & 64.792 & 63.75 & 62.292 & 63.611 \\ ML & 55.625 & 53.75 & 54.375 & 54.583 \\ JDA & 71.25 & 67.083 & 70.625 & 69.653 \\ TCA-DBN & 82.083 & 78.958 & 80.625 & 80.555 \\ DSAN & 86.875 & 84.167 & 88.125 & 86.389 \\ ITCA-DBN & 92.083 & 90.833 & 92.917 & 91.944 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Diagnosis results of the bearing faults in the drum motor. Figure 10: Radar chart of the bearing fault diagnosis results in the drum motor. As shown in Table 7 and Figure 11, the average time required for DBN, S-Alexnet, IG-CWT-CNN, TF-MDA, ICPW-HPF-CNN, ML, JDA, TCA-DBN, DSAN, and ITCA-DBN to complete three cross-working condition fault diagnosis tasks was 57.5 s, 608.7 s, 103.2 s, 722.3 s, 88.2 s, 53.7 s, 960.3 s, 207.6 s, 1797 s, and 365.4 s, respectively.