基于多重信号分类与模式搜索算法的笼型异步电动机转子断条故障检测新方法

A Detection Method for Broken Rotor Bar Fault in Induction Motors Based on Multiple Signal Classification and Pattern Search Algorithm

提出了一种基于多重信号分类(multiple signal classification,MUSIC)与模式搜索算法(pattern search algorithm,PSA)的异步电动机转子断条故障检测新方法。MUSIC方法对于短时信号具备高频率分辨力,可以准确计算转子断条故障特征分量以及其他分量的频率;但对诸频率分量幅值和初相角则无法准确求解。因此引入PSA确定诸频率分量的幅值、初相角,并对1台Y100L-2型3 kW笼型异步电动机完成了转子断条故障检测实验。实验结果表明:基于MUSIC与PSA的异步电动机转子断条故障检测方法切实可行,适用于负荷波动、噪声等干扰严重情况。

This paper proposed a detection method for rotor fault in induction motors, which was based on multiple signal classification （MUSIC） and pattern search algorithm （PSA）. The performance of MUSIC was tested with the simulated stator current signal of an induction motor with broken rotor bar fault. The results show that MUSIC was capable of identifying clearly the frequencies of the broken rotor bar fault feature components and the others in the simulated signal even with short-time sample, although it can not be used to handle with the amplitudes and initial phases of those components. PSA was introduced to determine the amplitudes and initial phases of the frequency components in the simulated signal and the results were really satisfactory. Thus paves the way to detect broken rotor bar fault in induction motors by combining MUSIC and PSA. The related experiment on a 3kW, Y100L-2-typed induction motor had been conducted, and the results reveal that the MUSIC-PSA-based rncthod to detect broken rotor bar fault in induction motors is effective even with short-time sample, and it is a promising choice for induction motors operating with fluctuant load or under severe interference.