Morlet小波解调制方法诊断鼠笼电机转子断条故障

发布时间：2018-11-27 17:58

【摘要】：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。
[Abstract]:In view of the characteristic analysis of motor current signal (motor current signature analysis,MCSA), there is a problem that the frequency spectrum leakage hinders the identification of fault characteristic frequency when diagnosing the fault of the rotor bar break in the early stage. A fault diagnosis method based on stator current Morlet wavelet de-modulation signal analysis is proposed. Firstly, the performance of Morlet wavelet is optimized by choosing appropriate parameters, and then the envelope of stator current signal of cage motor is extracted by the optimized Morlet wavelet to eliminate the influence of fundamental frequency and noise interference. Then the extracted envelope is analyzed by Fast Fourier transform (fast Fourier transform,FFT), and the fault of rotor broken bar is judged according to whether there is the characteristic frequency component 2sfs in the FFT spectrum. The proposed method can eliminate the influence of noise interference and spectrum leakage in the motor power frequency or frequency conversion power supply mode, so it is convenient to extract the fault features and realize the fault diagnosis of the rotor bar break in the early stage. Theoretical analysis and experimental results show that the proposed method is correct and effective.
【作者单位】：中国科学院沈阳自动化研究所;中国科学院大学;中国科学院网络化控制系统重点实验室;辽宁科技大学电子与信息工程学院;
【基金】：中国科学院重点部署项目(KGZD-EW-302)
【分类号】：TM343

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