摘要
提出了一种基于互高阶累积量的多信号分类法(Multiple Signal Classification,MUSIC)的异步电动机转子故障检测方法。由于断条故障特征频率分量易被基波分量泄漏及噪声淹没,而互高阶累积量可以有效抑制相关和非相关噪声,在混合噪声和信噪比很低(-20 dB)的情况下,该方法仍具有很高的谱分辨率和谱估计性能。仿真和试验结果表明,该方法在对电机转子断条故障检测时,在不需要对分析数据进行整周期采样的前提下,能准确检测出转子故障时电流中的故障特征成份,证明了该方法的有效性。
As cross high order accumulation is able to depress non-correlative noise and correlative gauss noises noise, MUSIC method based on cross-high-order cumulate is provided for diagnosing rotor broken-bar. It is difficult to detect the rotor broken-bar faults feature component as it always hides behind the strong supply frequency component in the spectrum of the stator current. Simulation results showed that the method is higher in resolution of frequency, more accurate in fault detection and less in computational complexity. And fault characteristic components can be obtained accurately through the method presented even with small samples. The feasibility of the method is confirmed.
出处
《电机与控制应用》
北大核心
2009年第6期5-9,共5页
Electric machines & control application
基金
吉林省杰出青年科研计划项目(20070129)
关键词
多信号分类
互高阶谱
转子断条故障
multiple signal classification
cross-high-order-spectrum
rotor broken-bar fault
