Abstract：This paper presents a detection method for rotor fault in induction motors, which is based on multiple signal classification(MUSIC) and simulated annealing algorithm(SAA). Firstly, the performance of MUSIC is tested with the simulated stator current signal of an induction motor with broken rotor bar fault. The results show that MUSIC is 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 handle with the amplitudes and initial phases of those components. Secondly, SAA is introduced to determine the amplitudes and initial phases of the frequency components in the simulated signal and the results are really satisfactory. Thus paves the way to detect broken rotor bar fault in induction motors by combining MUSIC and SAA. Finally, the related experiment on a 3kW, Y100L—2-typed induction motor is conducted, and the results demonstrate that the MUSIC-SAA-based method to detect broken rotor bar fault in induction motors is effective even with short-time sample, as makes it a promising choice for induction motors operating with fluctuant load or under severe interference.
孙丽玲, 许伯强, 李志远. 基于MUSIC与SAA的笼型异步电动机转子断条故障检测[J]. 电工技术学报, 2012, 27(12): 205-212.
Sun Liling, Xu Boqiang, Li Zhiyuang. A MUSIC-SAA-Based Detection Method For Broken Rotor Bar Fault in Induction Motors. Transactions of China Electrotechnical Society, 2012, 27(12): 205-212.
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