PD-Loc Dataset

Sample Text AbstractEnhancing electric machinery reliability, especially for high-powered propulsion systems in electric vehicles, aircraft, and ships, is imperative due to insulation defects being a leading cause of electrical machine failures. This research introduces an innovative diagnostic method that utilises acoustic sensor arrays in conjunction with the Generalised Cross-Correlation with Phase Transform (GCC-PHAT) and Time-Difference of Arrival (TDOA) algorithms for the precise localisation of Partial Discharge (PD) sources, marking the onset of insulation failure. Analytical experiments on a high-power switched reluctance motor (SRM), designed for traction, inform the optimal configuration of sensor arrays. A dedicated acoustic array was designed and fabricated, leading to the development of a 32-channel condenser microphone array-based acoustic source localisation system. This system was experimentally validated, achieving unprecedented sub-centimetre precision in PD source localisation. The accuracy of this approach is critical for early detection and mitigation of faults, significantly mitigating risks such as overheating and thermal runaway. Additionally, the creation of the PD-Loc Dataset, a comprehensive collection of acoustic signatures of PD events, stands as a valuable resource for the testing and validation of future PD diagnostic localisation techniques. This advancement marks a significant contribution to Condition-based Monitoring (CbM) systems, enhancing the maintenance and reliability of essential electric machinery.