摘要
分析了感应电机轴承发生故障时振动信号的特性以及MUSIC算法及其高分辨率谱估计的特点,提出了一种基于MUSIC算法的感应电动机轴承故障检测方法。结果表明,在短数据情况下,相对FFT分析技术,该方法频率分辨率更高,故障检测更为准确,且计算量小,有利于电机故障实时状态监测。实验证实,将该方法应用于感应电机轴承故障检测,可准确检测出轴承故障时在包络信号中的故障特征成分,方法切实可行。
The feature of vibration signal of defective rolling bearing, and a high - resolution spectral characteristics of MUSIC algorithm were analyzed. A method for detecting bearing faults in induction motors based on the MUSIC algorithm is presented. Compared with FFT analysis method, it is higher in resolution of frequency, more accurate in fault detection and less in computational complexity, which is useful in real - time monitoring. Experimental results show that the fault characteristic components can be obtained accurately through the method presented in this paper even with samples when the occurrence of bearing faults in induction motors, and that the feasibility of the method is confirmed.
出处
《电机与控制学报》
EI
CSCD
北大核心
2005年第4期392-396,共5页
Electric Machines and Control
关键词
感应电机
故障诊断
MUSIC算法
轴承
induction motor
fault diagnosis
MUSIC (Multiple Signal Classification) algorithm
bearing
