# 1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary
## 5 Results Analysis and Discussion
The results obtained via training of the conventional 1D CNN method, shown in Table 8, are used for comparison. These results show that both the conventional and integrated CNN-based methods are able to classify defective droppers. To compare the performance of the conventional 1D CNN and the integrated CNN, in the face of signal noise, the trend of the four metrics of the two methods with increasing noise power is shown in Figure 10. In Table 8, considering the four evaluating metrics of the integrated 1D CNN with the same noise power, the value is relatively consistent (the variance of these four values is lower than one). On the contrary, the variance of these values in the conventional 1D CNN is over 5% (at 25 dB). In terms of performance for the four metrics, the results of the integrated 1D CNN show better performance than the conventional 1D CNN. For example, under a 20 dB noise level, the accuracy of the integrated 1D CNN model is approximately 5% higher than that of the conventional 1D CNN model, which means there will be nine fewer false/missing alarms in terms of the selected testing dataset. Similar (though slightly \begin{table}
\begin{tabular}{c c c c} \hline \hline Noise power & \multicolumn{3}{c}{Accuracy (\%)} & F1-Score (\%) \\  & Conventional & Integrated & Conventional & Integrated \\ \hline
5 dB & 91.24 & 96.33 & 93.26 & 95.88 \\
10 dB & 90.57 & 95.28 & 92.31 & 95.08 \\
15 dB & 89.70 & 95.00 & 90.71 & 95.83 \\
20 dB & 88.46 & 93.94 & 87.69 & 94.84 \\
25 dB & 85.28 & 86.72 & 83.17 & 86.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between the conventional 1D CNN and the integrated 1D CNN model. \begin{table}
\begin{tabular}{c c c c} \hline \hline Noise power & \multicolumn{3}{c}{Precision (\%)} \\  & Sub-model 1 & Sub-model 2 & Integrated model \\ \hline
5 dB & 95.88 & 100 & 95.88 \\
10 dB & 95.08 & 100 & 95.08 \\
15 dB & 95.83 & 100 & 95.83 \\
20 dB & 94.84 & 100 & 94.84 \\
25 dB & 88.87 & 96.60 & 86.73 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline Noise power & \multicolumn{3}{c}{Precision (\%)} \\  & Sub-model 1 & Sub-model 2 & Integrated model \\ \hline
5 dB & 96.10 & 100 & 96.10 \\
10 dB & 95.07 & 100 & 95.07 \\
15 dB & 95.04 & 100 & 95.04 \\
20 dB & 93.93 & 100 & 93.93 \\
25 dB & 89.19 & 97.94 & 87.35 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline Noise power & \multicolumn{3}{c}{Recall (\%)} \\  & Sub-model 1 & Sub-model 2 & Integrated model \\ \hline
5 dB & 95.80 & 100 & 95.80 \\
10 dB & 95.25 & 100 & 95.25 \\
15 dB & 94.69 & 100 & 94.69 \\
20 dB & 93.58 & 100 & 93.58 \\
25 dB & 88.68 & 97.55 & 86.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(Cont.\)smaller) improvements can be seen across the three other metrics. This demonstrates that the integrated 1D CNN is more effective in classifying the fault with two categories (i.e., damage degree and location). When the noise power increases, the values of the four performance metrics reduce. The maximum noise used in this study was 25 dB, which is more than the maximum permissible measurement error. When the noise power is 25 dB, the performance of the four values is still over 85%. This indicates that contact-force-based condition monitoring is effective in the early detection of railway catenary dropper damage. Additionally, the computational cost is a factor of concern. In this research, the computation is based on the Google Collaboratory platform with a P100 GPU. By training the conventional 1D CNN model and the integrated 1D CNN model with their own optimal hyper-parameters, the computational costs from the two methods are compared. The computational time of the integrated 1D CNN model is increased by 4% compared with the conventional 1D CNN model.