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首页> 外文期刊>IEEE Transactions on Instrumentation and Measurement >Incipient Fault Detection in Stator Windings of an Induction Motor Using Stockwell Transform and SVM
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Incipient Fault Detection in Stator Windings of an Induction Motor Using Stockwell Transform and SVM

机译:使用Stockwell变换和SVM在感应电动机定子绕组中的初始故障检测

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In this article, Stockwell transform (ST) is used to analyze the stator current signals for diagnosis of various motor conditions such as healthy, stator winding interturn shorts, and phase to ground faults. ST decomposes the current signals into complex ST matrix whose magnitude has been utilized for the fault detection. The nature of the fault, that is, ground or interturn is identified using the zero sequence currents followed by postfault detection. Two separate frequency bands are defined to extract the features which are fed to two different support vector machine (SVM) models for faulty phase detection for both types of faults. Under both cases, a heuristic feature selection approach is utilized to find the optimal features for classification purposes. Average classification accuracy of 96% has been achieved for both types of faults.
机译:在本文中,斯托克尔变换(ST)用于分析定子电流信号,以诊断各种电动机条件,如健康,定子绕组干扰短路和相位到接地故障。 ST将当前信号分解成复杂的ST矩阵,其幅度已用于故障检测。故障的性质,即使用零序电流识别后台,后跟检测。定义了两个单独的频带以提取馈送到两种不同支持向量机(SVM)模型的特征,用于两种故障的故障相位检测。在这两种情况下,利用启发式特征选择方法来查找分类目的的最佳特征。两种类型的故障都已经实现了96%的平均分类准确性。

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