小波-奇异值分解在异步电机转子故障特征提取中的应用

A FEATURE EXTRACTION METHOD OF ROTOR FAULTS OF INDUCTION MOTOR BASED ON CONTINUOUS WAVELET TRANSFORM AND SINGULAR VALUE DECOMPOSITION

针对电流信号中异步电机的转子故障特征分量经常被电源频率分量淹没而无法准确检测的缺点,提出了一种基于小波-奇异值分解的转子故障特征提取方法。通过连续小波变换将电流信号中的各特征频率分量转换到时频分布空间中,对该时频空间进行奇异值分解将各特征频率分量分解到不同的正交特征子空间中,对特征子空间的选择重构可以有效地滤除电源频率分量而提取出转子故障特征分量。模拟数据和实际故障信号的应用表明,该方法提供了一种可实际应用的异步电机转子故障诊断方法。

As it is always submerged by the supply frequency component in current signal, the rotor fault feature component is difficultly detected. A novel feature extraction method of rotor faults based on continuous wavelet transform （CWT） and singular value decomposition （SVD） is proposed. The current signal which consists of the supply frequency component and the rotor fault feature components is transformed into the time-frequency space by CWT, and the time-frequency information is decomposed to a series of orthogonal sub-spaces by SVD, which include different feature components. With the reconstruction of the sub-spaces selected, the rotor fault feature components are extracted effectively. Experimental results and industrial measurement analysis show that the approach provides a more effective means to diagnose rotor faults.