PD-Loc Dataset
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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.
示例摘要：提升电机可靠性，尤其是电动汽车、航空器与船舶的大功率推进系统用电机的可靠性，实属必要，因为绝缘缺陷是电机故障的首要诱因。本研究提出一种创新诊断方法，将声学传感器阵列（acoustic sensor arrays）与广义互相关相位变换（Generalised Cross-Correlation with Phase Transform, GCC-PHAT）及到达时间差（Time-Difference of Arrival, TDOA）算法相结合，实现对局部放电（Partial Discharge, PD）源的精准定位——局部放电正是绝缘失效的早期征兆。针对一款专为牵引应用设计的大功率开关磁阻电机（switched reluctance motor, SRM）开展分析实验，以此确定传感器阵列的最优配置方案。研究人员设计并搭建了专用声学阵列，进而开发出一套基于32通道电容传声器阵列的声源定位系统。通过实验对该系统进行验证，其在PD源定位任务中实现了前所未有的亚厘米级精度。该方法的定位精度对于故障的早期检测与抑制至关重要，可显著降低过热与热失控等安全风险。此外，本研究还构建了PD-Loc数据集，这是一套涵盖PD事件声学特征的综合数据集，可为未来PD诊断定位技术的测试与验证提供宝贵的研究资源。这项进展为基于状态的监测（Condition-based Monitoring, CbM）系统作出了重要贡献，可提升关键电机设备的运维水平与可靠性。
