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Information fusion with Correlation Coefficient for detecting inter-turn short circuit faults in asynchronous machines

机译:具有相关系数的信息融合在异步电机中检测匝间短路故障

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This paper presents a new method giving high efficiency for detecting an inter-turn short-circuit fault in the stator winding of asynchronous machines. For evaluation of the machine state and final decision, the monitoring of the magnetic field variation in the vicinity of an electrical machine is used. The proposed approach is based on the fusion of information extracted from signals delivered by flux sensors placed in different positions around the machine and the calculation of Pearson correlation coefficient. This coefficient allows one to quantify the linear relationship between the signals delivered by two sensors S1 and S2 placed at 180° around the machine in several positions. The proposed approach is non-invasive and relies on the calculation of a correlation coefficient derived from measurements of the external magnetic leakage field for different load working cases. The ability of proposed coefficient to provide useful information about faults is investigated in the paper.
机译:本文提出了一种新的方法,该方法可以有效地检测异步电机定子绕组中的匝间短路故障。为了评估电机状态和最终决策,使用了对电机附近磁场变化的监控。所提出的方法是基于融合信息的,该信息是从通过放置在机器周围不同位置的磁通传感器传递的信号中提取的信息进行融合,并计算了皮尔森相关系数。这一系数可以量化由两个传感器S1和S2传递的信号之间的线性关系,两个传感器S1和S2在多个位置围绕机器以180°放置。所提出的方法是非侵入性的,并且依赖于相关系数的计算,该相关系数是从针对不同负载工况的外部漏磁场的测量得出的。本文研究了提出的系数提供有关故障的有用信息的能力。

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