## 4 Results
### Testing CNN Model with One-Hour Measurement Duration  
A wider range of PD events could be captured by extending the measurement duration, including those occurring during inrush surges and subsequent stable periods, as discussed in Section 4.2. This provided a more comprehensive understanding of the defect patterns and their characteristics under different voltage conditions. Consequently, the CNN model trained on longer-duration PRPD patterns became more robust and capable of handling variations in voltage levels.  
Defect recognition accuracy was improved by increasing the measurement duration to one hour for each voltage from the lower RPDIV to the PDEV. This decision was made based on the observation that the condition of one-minute-duration PRPD patterns did not yield satisfactory results.  
The choice of one hour as the measurement duration was influenced by the maximum duration of the PD analyzer used in the experiment. This longer duration provided a more comprehensive capture of the PD activity and a greater amount of data for analysis. The PRPD patterns of these test samples for the one-hour duration are depicted in Figure 11.  
Similar to previous conditions, it was clear that the visibility and stability of the patterns improved as the voltage increased, as shown from the analysis of the PRPD pattern shapes presented in Figure 11. The defect recognition accuracy of the CNN model during defect-type testing was significantly enhanced compared to similar test results from the one-minute condition, as summarized in Table 3.  
Figure 11: PRPD patterns for one-hour duration at different void positions.  
The overall testing accuracies for Void_Top, Void_Bottom, and Void_Center were all 100% when considering the lower RPDIV values up to the PDEV. This indicated that the CNN model successfully classified the PRPD patterns for all three defect types with perfect accuracy.  
Moreover, the testing accuracies for Void_Top, Void_Bottom, and Void_Center remained at 100% when focusing specifically on the PDEV and PRPD patterns. This implies that the CNN model correctly identified the defect types without any misclassifications, even at the lowest voltage level (PDEV).  
The highlight of these results is that the PRPD measurements for the transition from the one-minute to one-hour duration led to improved accuracy in defect recognition. The CNN model achieved perfect classification accuracy for all defect types, demonstrating its enhanced ability to differentiate between the PRPD patterns. Additionally, the robust generalization of the model was evident, as it maintained perfect accuracy at the lowest voltage level (PDEV). The extended measurement duration allowed the capture of more distinct PRPD patterns, resulting in enhanced discrimination and improved defect recognition across different void positions in the epoxy resin samples.  
These findings underscore the need to consider and analyze weak PD signals, even at the PDEV, as they can provide valuable insights for reliable defect recognition. On the other hand, the measurement duration should be set longer than one minute when the PD signal was observed intermittently or even never.