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基于高频率分辨力谱估计技术与优化算法的异步电动机转子故障检测新方法

, PP. 140-147

Keywords: 异步电动机,转子故障检测,高频率分辨力谱估计技术,优化算法,多重信号分类,旋转不变信号参数估计技术,模拟退火算法,模式搜索算法

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Abstract:

将高频率分辨力谱估计技术与优化算法相结合而提出一种新的异步电动机转子故障检测方法。针对两种典型的高频率分辨力谱估计技术——多重信号分类(multiplesignalclassification,MUSIC)与旋转不变信号参数估计技术(estimationofsignalparametersviarotationalinvariancetechnique,ESPRIT),应用模拟转子故障的定子电流信号测试其频率分辨力、精度等性能,结果表明:即使对于短时信号,二者仍具高频率分辨力,可以准确地分辨定子电流信号中转子故障特征分量、主频分量之频率;但对其幅值、初相角,仅能提供“粗糙”估计。为此,尝试以优化算法——模拟退火算法(simulatedannealingalgorithm,SAA)与模式搜索算法(patternsearchalgorithm,PSA)确定各分量的幅值与初相角。同时,分别对MUSIC与ESPRIT、SAA与PSA做了性能对比,遴选优者并应用于转子故障检测。最后,针对转子断条故障进行实验,结果表明:基于高频率分辨力谱估计技术与优化算法的异步电动机转子故障检测方法有效、可行,即使在负载波动、噪声等干扰严重情况下仍然适用。

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