# Constructing Condition Monitoring Model of Harmonic Drive
## 4 Methods
### The Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 3**} \\  & **Log Mel Spectrogram** & \multicolumn{2}{c}{**Log Mel Spectrogram**} & \multicolumn{2}{c}{**Log Mel Spectrogram**} \\  & **+ Wavelet Transforms** & \multicolumn{2}{c}{**+ EfficientNetV2S**} & \multicolumn{2}{c}{**+ Wavelet Transforms**} \\  & **+ EfficientNetV2S** & \multicolumn{2}{c}{**+ MobileNetV2**} \\ \hline  & **Source\_test** & **Target\_Test** & **Source\_Test** & **Target\_Test** & **Source\_Test** & **Target\_Test** \\ \hline ID\_01 & 0.7176 & 0.6804 & 0.6968 & 0.7032 & 0.6484 & 0.6884 \\ \hline ID\_02 & 0.7992 & 0.7579 & 0.67 & 0.6291 & 0.6554 & 0.5802 \\ \hline ID\_03 & 0.8573 & 0.5222 & 0.8384 & 0.4454 & 0.717 & 0.6452 \\ \hline Average & 0.7914 & 0.6535 & 0.7351 & 0.5926 & 0.6736 & 0.6379 \\ \hline Average of two domains & \multicolumn{2}{c}{0.7224} & \multicolumn{2}{c}{0.6638} & \multicolumn{2}{c}{0.6558} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance of the proposed gearbox anomaly detection models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**This Paper**} & \multicolumn{2}{c}{**Log Mel Spectrogram**} & \multicolumn{2}{c}{**Log Mel Spectrogram**} \\  & & & **+ AutoEncoder** [4] & & **+ MobileNetV2 [4]** \\ \hline
**Gearbox** & **Source\_Test** & **Target\_Test** & **Source\_Test** & **Target\_Test** & **Source\_Test** & **Target\_Test** \\ \hline ID\_01 & 0.7176 & 0.6804 & 0.6463 & 0.6479 & 0.6354 & 0.6702 \\ \hline ID\_02 & 0.7992 & 0.7579 & 0.6766 & 0.5812 & 0.6668 & 0.6696 \\ \hline ID\_03 & 0.8573 & 0.5222 & 0.7538 & 0.6557 & 0.8087 & 0.4315 \\ \hline Average & 0.7914 & 0.6535 & 0.6922 & 0.6283 & 0.7037 & 0.5904 \\ \hline Average of two domains & \multicolumn{2}{c}{0.7224} & \multicolumn{2}{c}{0.6603} & \multicolumn{2}{c}{0.6471} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with other studies on gearbox samples. This research explored the impact of sample duration and noise on prediction performance. Based on duration, the samples were categorized into one second and three seconds. The results of the prediction accuracy of different sample durations are shown in Table 11. The prediction AUC of the monitoring model for the three-second category was lower than that for the one-second category, possibly because the model needs a larger number of samples to detect the pattern of the anomaly sounds. Regarding the noise, additive white Gaussian noise was added to the 1 s duration sound samples. The two kinds of SNRs for the experiments were of the 20 dB(Lin) and 10 dB(Lin) categories. The prediction AUC results with different SNRs in this research are shown in Table 12.  
Table 12 shows that the intensities of noise and the prediction AUC results of the model are related. In the case of noise addition, the model could still maintain a good \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy** & **AUC** \\ \hline This paper & 0.901 & 0.9302 \\ [PERSON] et al. [16] & 0.91 & 0.911 \\ [PERSON] et al. [17] & 0.858 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the proposed model with other models when using the Harmonic Drive Dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**AUC**} \\ \hline Samples with a 1-s duration & 0.9302 \\ Samples with a 3-s duration & 0.881 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The experiment results of different sample durations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**AUC**} \\ \hline Samples without adding noise & 0.9302 \\ SNR = 20 dB (Lin) & 0.87 \\ SNR = 10 dB (Lin) & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The experiment results of different SNRs.