中文摘要：

将精简四阶累积量（Streamlined Fourth-Order Cumulants，SFOC）多重信号分类法（Mulfiple Signal Classification，MUSIC）与混合遗传算法（Hybrid Genetic Algorithm，HGA）结合，提出一种新型异步电机转子断条检测方法。利用基于四阶累积量的MUSIC可有效抑制噪声，扩展信号阵元，改善频谱估计性能，以高频率分辨率提取定子电流信号中的转子断条故障特征分量及主频分量的频率；利用混合遗传优化算法估计各频率分量的幅值和初相位。为提高算法的快速性，根据四阶累积量矩阵构成规律，提出一种改进方法去除矩阵数据冗余，极大减小了计算量；针对遗传算法易早熟和后期收敛较慢的缺点，引入单纯形法。仿真与试验验证了新方法在低信噪比和短时采样时间情况下仍具有较高的频谱分辨率和估计精度。最后与FfYr、MUSIC-SAA法进行了比较，证明了其优越性。

英文摘要：

A new method for detecting broken rotor bar fault （BRB） in induction motors was proposed which was based on the combination of streamlined fourth-order cumulants, Multiple Signal Classification （MUSIC） and hybrid genetic algorithm. MUSIC based on fourth-order cumulants could effectively reduce the noise interference, extend signal array, improve spectral estimation performance and hence, extract BRB feature component and power frequency component with a high frequency resolution; and then try to apply the Hybrid Genetic algorithm to determine the amplitude and the initial phase of each frequency component. Additionally, in order to speed up MUSIC based on fourth-order cumulants, a reduction algorithm was proposed to eliminate redundant data of the matrix according to the composition method of fourth-order cumulants matrices, thus greatly reducing the calculation amount of this kind of MUSIC. To cope with the premature and slow convergent rate, a global optimization method was presented, which shared the advantages of both genetic and simples. A series of simulations and experiments were conducted to verify the presented detection method＇s high spectral resolution and estimation precision in the low SNR and short sampling time. In the end, comparing with FFT, MUSIC-SAA method proved the presented detection method＇s superiority.