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An ESPRIT-SAA-Based Detection Method for Broken Rotor Bar Fault in Induction Motors

机译:基于ESPRIT-SAA的感应电动机转子断条故障检测方法。

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摘要

This paper presents a novel detection method for broken rotor bar fault (BRB) in induction motors based on the estimation of signal parameters via rotational invariance technique (ESPRIT) and simulated annealing algorithm (SAA). The performance of ESPRIT is tested with the simulated stator current signal of an induction motor with BRB. It shows that even with short-time measurement data, the technique is capable of correctly identifying the frequencies of the BRB characteristic components but with a low accuracy on the amplitudes and initial phases of those components. The SAA is then used to determine their amplitudes and initial phases and shows satisfactory results. Finally, experiments on a 3-kW, 380-V, 50-Hz induction motor are conducted to demonstrate the effectiveness of the ESPRIT-SAA-based method in detecting BRB with short-time measurement data. It proves that the proposed method is a promising choice for BRB detection in induction motors operating with small slip and fluctuant load.
机译:本文提出了一种基于旋转不变技术(ESPRIT)和模拟退火算法(SAA)的信号参数估计方法,用于感应电动机转子断条故障(BRB)的检测。 ESPRIT的性能通过带有BRB的感应电动机的仿真定子电流信号进行测试。它表明,即使具有短时测量数据,该技术也能够正确识别BRB特征分量的频率,但这些分量的幅度和初始相位的准确性较低。然后使用SAA确定其幅度和初始相位,并显示令人满意的结果。最后,在3kW,380V,50Hz感应电动机上进行了实验,以证明基于ESPRIT-SAA的方法在利用短时测量数据检测BRB方面的有效性。证明了所提出的方法是在小滑差和波动负载下运行的感应电动机中BRB检测的有前途的选择。

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