## 4 Results and Discussions  
The validation results are provided in Table 3. It can be shown from Table 3 that if the CNN with AE signals were used to diagnose the gear pitting faults, high diagnostic accuracy would be obtained for training. Although the accuracy of the training is very high, the method has a serious overfitting phenomenon, and as a result, the accuracy of the testing is as low as 74.57%. Using the same CNN with AE signals and GRUs with vibration signals, not only good training and verification results were obtained, but also accurate gear pitting fault diagnosis results of 98.29% were obtained for testing. The results show that in comparison with other methods, the method presented in this paper can obtain diagnostic results more effectively. Figure 8: The general procedure of the data processing using the presented method. The results in Table 3 indicate that using the combination of CNN with AE signals and GRU with vibration signals or the combination of CNN with vibration signals and GRU with AE signals gave more accurate results than the following three methods: (1) using CNN with both vibration and AE signals, (2) using CNN with vibration signals alone, and (3) using CNN with AE signals alone. For other methods in Table 3, the followings are the discussions regarding why some methods could not achieve a good gear pitting fault diagnosis accuracy. For the GRU network with vibration signals alone, it might be because the vibration signals were greatly interfered by the background noises, and the GRU has the ability to memorize the signals and hence possibly retain the noisy features. So the atypical feature of the received interference was regarded as a typical feature of the gear pitting faults. As a result, it might affect its effectiveness. For the GRU network with AE signals alone, since the sampling frequency of the AE signals is much than higher than the vibration signals, the sampled data of the AE signals was huge. In order to process the AE signals efficiently using the GRU network, the AE signal data was down sampled. So the data was partially distorted. As a result, the accuracy of the gear pitting fault diagnosis was low. In Table 4, the gear pitting fault diagnosis accuracies for each pitting fault condition obtained by the proposed method and other methods are provided. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**The Accuracy of each Condition Fault Pattern**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Proposed method: CNN with AE signals + GRU network with vibration signals & 100\% & 94\% & 100\% & 100\% & 100\% & 94\% & 100\% \\ \hline CNN with vibration signals + GRU network with AE signals & 100\% & 96\% & 98\% & 100\% & 100\% & 94\% & 98\% \\ \hline CNN with vibration signals + CNN with AE signals & 100\% & 94\% & 98\% & 100\% & 100\% & 78\% & 100\% \\ \hline CNN with vibration signals alone & 86\% & 82\% & 94\% & 100\% & 100\% & 82\% & 96\% \\ \hline GRU network with vibration signals alone & 84\% & 82\% & 86\% & 100\% & 100\% & 82\% & 94\% \\ \hline CNN with AE signals alone & 100\% & 82\% & 74\% & 84\% & 52\% & 34\% & 96\% \\ \hline GRU network for AE signals alone & 98\% & 46\% & 42\% & 66\% & 40\% & 44\% & 96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gear pitting fault diagnosis accuracy for each fault condition.