## 1 Introduction  
Partial discharges (PDs) occur in electrical insulation systems and indicate the presence of defects or degradation within the insulation material [1; 2; 3]. According to IEC 60270, a PD is referred to as an electrical pulse-type discharge that partially bridges the insulation [4]. It is a critical parameter to monitor because excessive PD activity can lead to insulation breakdown and failure [5; 6]. In recent years, the development of advanced measurement techniques and defect recognition methods has been instrumental in improving the accuracy and reliability of PD detection [7; 8]. One method that has gained significant attention is phase-resolved partial discharge (PRPD) analysis. PRPD analysis involves capturing and analyzing the patterns of PD pulses in a time domain, allowing for a more comprehensive understanding of PD behavior and improved defect recognition capabilities [9]. Valuable insights can be gained regarding the condition of the insulation material by examining the characteristics of PD pulses from a partial discharge inception voltage (PDIV) to a partial discharge extinction voltage (PDEV). The accurate detection and characterization of defects are of the utmost importance in the field of epoxy resins that are widely used as insulation materials in various electrical applications [10; 11; 12]. Artificially fabricated voids are commonly used as representative defects to simulate the imperfections that occur in epoxy resins. The positions of these voids within the resin can significantly influence the PD characteristics and, consequently, the accuracy of defect recognition. In this study, the positions of the voids in the epoxy were placed at the top, center, and bottom. [PERSON] et al. increased the voltage value step by step above the repetitive partial discharge inception voltage (RPDIV) level, where sporadic bursts of PD can occur. These bursts meet the conditions for RPDIV. However, the PD activity is unstable and may be extinguished. The current definition of RPDIV in international standards does not account for PD bursts. To address this issue, conditioning the insulation system above the RPDIV level is proposed as a suitable approach to reduce the likelihood of observing PD bursts [13; 14]. This study provides recommendations for performing more accurate RPDIV measurements by combining the understanding of PD activity behavior with the impact of conditioning. Different procedures were followed in this study. Specifically, the voltage value was decreased step by step from below the RPDIV level to the PDEV level. Subsequently, it was observed that PDs occurred at a very low level until the PRPD pattern cannot be recognized for a long time, up to 1 h. This observation raised questions regarding the influence of weak PDs on defect recognition during RPDEV measurements. Combining the PRPD method with advanced techniques, such as convolutional neural networks (CNNs), has shown promising results for defect recognition [15; 16]. However, the traditional approaches often overlook the significance of weak PD signals in the PDEV. It is crucial to highlight that neglecting weak PDs in the PDEV can lead to missed detections and compromised accuracy in defect recognition. Therefore, this study aimed to clarify the potential of weak PDs by exploring the effects of an extended measurement duration and CNN-based analysis on PD analysis from the PDIV to the PDEV. In addition, this research aimed to enhance defect recognition capabilities and improve the reliability of high-voltage equipment diagnostics by emphasizing the importance of capturing weak PDs in the PDEV. As mentioned previously, a CNN was employed to analyze and interpret PD data, which is a deep learning model known for its effectiveness in pattern recognition tasks. The CNN model was trained and tested using PRPD data collected under two measurement conditions. PD data that were recorded at the PDIV for a duration of one minute were utilized to train the CNN.