基于能量频谱分析法的电机故障诊断

摘要

When troubleshooting asynchronous motor based on stator current signal, the broken rotor bar and turn to turn short-circuit fault characteristics frequency components was often overwhelmed by current fundamental frequency component. By analyzing of rotor bar breaking fault and turn to turn short-circuit fault principle, diagnosis using Wavelet energy spectrum proves to be very necessary. Taking into account the ability of wavelet analysis to show the local characteristics of signals in time and frequency domain, the ability to highlight different frequency components of the signals through the flexible transform time-frequency window, and the useful wavelet processing advantages of non-stationary signals,the energy of every band was calculated and the fault features of energy distribution were obtained. Practice shows that by the above analysis and from the perspective of energy distribution, the motor fault can be quickly and accurately diagnosed, and the diagnostic quality is better than Fourier analysis, thus, providing a theoretical basis for online real-time fault diagnosis of motors.%基于定子电流信号进行异步电机故障诊断时,转子断条和匝间短路故障特征频率分量常常被电流的基频分量淹没,利用小波变换的能量谱对其进行诊断十分必要.考虑到小波分析在时域、频域都具有表征信号局部特征的能力,能通过时频窗的灵活变换来突出信号的不同频率成分；同时还有用小波处理非平稳信号的优越性,计算出各频带所占能量,进而获取能量分布的故障特征.实践表明,通过上述分析并从能量分布的角度出发,可以快速、准确地诊断出电机故障,诊断效果好于傅里叶分析；同时也提供了一种思路,为电机故障在线实时诊断提供了理论依据.