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Partial discharge (PD) is a phenomenon of electric discharge typically caused by the damaged or aged insulation of high voltage equipment in power grids, such as transformers, switch gears, and cable terminals. In the context of Prognostic and Health Management (PHM), detection and monitoring of PD are important to ensure the reliability of electrical assets and to avoid catastrophic failures. Machine learning techniques have been successfully applied to discover features and patterns that correspond to different types of partial discharges [9], [11]. Recently, PD monitoring systems have being deployed for assessing the health condition of these equipments continuously so that the maintenance would require less human effort and fewer maintenance interruptions to the operation. However, such systems require labeled data to build data models for PD detection and classification. Labeled data is expensive to …