# Constructing Condition Monitoring Model of Harmonic Drive
## 4 Methods
### The Experimental Results  
The harmonic drive is a type of gearbox, so the gearbox audio samples could be used to evaluate the anomaly detection model for the harmonic drive. For the sample source of the gearbox, the DCASE 2022 Dataset gearbox audio samples were used. The model with the highest AUC was selected by comparison through experiments. Three models were proposed in this study for gearbox machinery anomaly detection. The AUC of the three models is shown in Table 7. Model one, which combined logarithmic Mel spectrogram, wavelet transforms, and the EfficientNetV2S network architecture, achieved the best prediction performance. Thus, this research used model 1 for subsequent comparisons with methods proposed in other studies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**This Paper** & **DCASE 2022** & **Harmonic Drive** \\
**Dataset** & **Dataset** & **Dataset** \\ \hline Training Epochs & 200 & 200 \\ Optimizer & Adam & Adam \\ Learning Rate & 0.001 & 0.001 \\ Input Image Size & 64 \(\times\) 128 & 64 \(\times\) 128 \\ Log Mel Spectrogram Frame Size & 1024 & 1024 \\ Batch Size & 128 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyperparameters of the monitoring model. Figure 8: The architecture of the MB convolution. Figure 9: The architecture of the Fused-MB convolution. The proposed model outcomes were compared with those of two baseline models provided by the organizers of the DCASE 2022 Challenge. The results presented in Table 8 show that the average AUC of the proposed model was about 6% higher than those of the two baseline models, suggesting that the proposed model performed better than the two baseline models for gearbox anomaly detection tasks in real factory scenarios. Further, the model's performance in fault detection tasks was examined for various types of machinery in the DCASE2022 Dataset to assess whether the proposed model could detect general machinery anomalies. The results in Table 9 suggest a good capability of the proposed model for the detection of various types of machinery anomalies. The overall AUC was 5% higher than that of baseline models on average, and the AUC of the Slider category was nearly 20% higher. After the proposed model was evaluated on the DCASE 2022 Dataset, the Log Mel spectrogram, discrete wavelet transforms, and EffentNetV2S network were used to build the harmonic drive anomaly monitoring model. As the training set of the experiment, we randomly selected 60% of the data from the normal samples and abnormal samples of the Harmonic Drive Dataset and considered 20% of the data as the validation set; the remaining 20% of the data was used as the test set. In this research, samples of 1 s duration were used for the experiments. The proposed model was compared with other models for rotating machinery anomaly sound detection, including the method that uses the Fast Kurtogram combined with deep convolution to predict bearing anomalies proposed by [PERSON] et al. [16] and our previous research [17] that uses the wavelet transforms combined with the fully connected network to predict the wind turbine blade anomalies. The results are shown in Table 10.