首页> 外文会议>EVER 2013;International Conference and Exhibition on Ecological Vehicles and Renewable Energies >Classification and Diagnosis of Broken Rotor Bar Faults in Induction Motor using Spectral Analysis and SVM
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Classification and Diagnosis of Broken Rotor Bar Faults in Induction Motor using Spectral Analysis and SVM

机译:光谱分析和SVM对感应电动机断裂杆断层的分类和诊断

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In this paper, we propose to detect and localize the broken bar faults in multi-winding induction motor using Motor current signature (MCSA) combined to Support Vector Machine(SVM). The analysis of stator currents in the frequency domain is the most commonly used method, because induction machine faults often generates particular frequency components in the stator current spectrum. In order to obtain a more robust diagnosis, we propose to classify the feature vectors extracted from the magnitude of spectral analysis using multi-class SVM to discriminate the state of the motor. Finally, in order to validate our proposed approach, we simulated the multi-winding induction motor under Matlab software. Promising results were obtained, which confirms the validity of the proposed approach.
机译:在本文中,我们建议使用电机电流签名(MCSA)来检测和定位多绕组感应电动机中的断路器故障,组合为支持向量机(SVM)。 频域中定子电流的分析是最常用的方法,因为感应机故障通常在定子电流频谱中产生特定的频率分量。 为了获得更强大的诊断,我们建议使用多级SVM来分类从光谱分析的大小提取的特征向量,以区分电机的状态。 最后,为了验证我们所提出的方法,我们模拟了Matlab软件下的多绕组感应电机。 获得了有希望的结果,证实了所提出的方法的有效性。

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