基于Daubechies小波分析的MCSA感应电机轴承故障估计

K. C. Deekshit Kompella, M. V. Gopala Rao, R. S. Rao, R. N. Sreenivasu
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引用次数: 19

摘要

提出了一种基于电流的感应电机轴承故障监测方法。与传统的振动监测相比,无传感器监测具有许多优点。然而,由于感应电机的负载和转速变化,该方法的性能不佳。由于定子电流的非平稳特性,可能会出现傅立叶变换问题。因此,本文提出了用小波分析进行电机电流特征分析,并与FFT分析进行了比较。该方法已应用于三相异步电动机轴承故障的检测。从定子电流谱中提取轴承故障分量比较困难,特别是在初始阶段。因此,这里可以通过从定子电流特征中消去非轴承故障分量来识别轴承故障。实验结果证实了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of bearing faults in induction motor by MCSA using Daubechies wavelet analysis
This paper presents the current based monitoring of induction motor for identification of bearing faults. Sensor less monitoring has many advantages over conventional vibration monitoring. The method however does not give good performance due to variable load and speed of induction motor. Due to non stationary nature of stator current, Fourier transform problems may occur. Therefore, this work presents the motor current signature analysis using wavelet analysis and compares with the FFT analysis. The proposed method has been applied to detect the bearing faults in 3 phase induction motor. It is difficult to extract the bearing fault component from stator current spectrum especially at incipient stage. Therefore, here the bearing fault can be identified by cancelling nonbearing fault component from stator current signature. The results have affirmed the effectiveness of the method.
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