Morlet小波解调制方法诊断鼠笼电机转子断条故障

Diagnosis of broken rotor bars in squirrel cage induction motors using Morlet wavelet demodulation techniques

针对电机电流信号特征分析(motor current signature analysis,MCSA)诊断早期转子断条故障时存在的频谱泄露阻碍故障特征频率识别的问题,提出一种基于定子电流Morlet小波解调制信号分析的故障诊断方法。首先选择合适的参数对Morlet小波性能进行优化,继而利用优化后的Morlet小波提取鼠笼电机定子电流信号包络线以消除基频和噪声干扰的影响,然后对提取到的包络线作快速傅里叶变换(fast Fourier transform,FFT)分析,并根据FFT频谱中是否存在特征频率成分2sfs判断转子断条故障发生与否。所提方法在电机工频或变频供电方式、不同负载运行状况下都能够消除噪声干扰和频谱泄露影响,因而便于故障特征提取并实现早期转子断条故障诊断。理论分析和实验结果表明了所提方法的正确性和有效性。

Spectrum leakages make it difficult to identify fault feature during early diagnostics of squirrel cage induction motor broken rotor bars with motor current signature analysis （ MCSA） method. A diagnosis method based on Morlet wavelet demodulated current signal was proposed to overcome this problem. The fast Fourier transform （ FFT） spectrum of the envelope of stator current was investigated and the conclusion of failure or not was drawn depending on the situation that characteristic frequency of 2sfs could be found in obtained spectrum. The envelope extraction was performed by Morlet wavelet demodulation algorithm with proper selected wavelet parameters and used to remove the strong fundamental frequency and additional components outside the frequency region of interest. For main-fed or inverter-fed motor operating under different load conditions, the proposed method can all eliminate noises and fundamental frequency, which make feature extraction easier for the purpose of incipient diagnosis of broken rotor bars. The theoretical analysis and experimental results show the effectiveness of the proposed methods.