基于MUSIC与FOA的异步电动机转子断条故障检测

A Detection Approach Based on MUSIC and FOA for Broken Rotor Bar Fault in Induction Motors

为了更加准确和快速检测异步电动机转子断条特征分量的频率和幅值,通过结合多重信号分类(MUSIC)与果蝇优化算法(FOA)并将其用于异步电机转子断条故障检测。作为一种具有高频率分辨力的谱估计技术,MUSIC能够快速而准确地检测边频以及基频分量的频率大小,即使对于短时采样信号也有良好的性能。然而,MUSIC虽是提取信号频率的有效工具,却没有能力计算各个分量的幅值和相位。因此,该文引入FOA以确定各个分量的幅值和相位,结果表明性能良好。通过仿真和实验验证了基于MUSIC与FOA的异步电动机转子断条故障检测方法的有效性。

A novel technique was presented to improve the accuracy and rapidity of tracking the broken rotor bar char- acteristic frequencies and amplitudes in induction motors by combining multiple signal classification（MUSIC） and fruit fly optimization algorithm（FOA）. MUSIC, a high-resolution spectral analysis technique, the frequencies of sidebands could be tracked as well as the fundamental frequency component with a very high accuracy even with a short-time sample. How- ever, MUSIC was a powerful tool extracting meaningful frequencies from the signal, has not ability to estimate the ampli- tudes and phase angles of those components. As a solution for this problem, FOA was introduced to determine the ampli- tudes and phase angles of the frequency components in the simulated signal and the results have a superior performance. Simulations and experiment verify the effectiveness and superiority of the proposed method.