基于复Morlet小波的汽车主减速器故障特征提取

Fault Feature Extraction of Automobile Main Reducer Based on Complex Morlet Wavelet Transform

提出了基于复Morlet小波的汽车主减速器故障特征提取方法。针对汽车主减速器故障振动信号的特点,结合复小波变换提供的幅值和相位信息构造了两组适合于机械故障特征提取的组合信息。仿真信号的分析结果表明,采用复小波变换的相位信息及所构造的组合信息对信号突变点具有更好的敏感特性,从而可以更好地对信号突变点进行提取和定位。分别采用实小波变换和复小波变换及其组合信息对汽车主减速器故障信号进行分析。分析结果表明,利用所构造的组合信息能够对主减速器故障特征点精确定位;而且只需一尺度小波分解即可得到较好的效果,从而大大减小了故障特征提取的计算量。

Introducing the complex wavelet transform into the fault feature extraction of automobile main reducer, a fault feature extraction method for automobile main reducer based on complex Morlet wavelet transform was proposed. Aimed at the characteristics of main reducer vibrating signal, two groups of compounding information for the extraction of mechanical fault feature were constructed according to the magnitudes and phases of complex wavelet transform. The simulation results show that the phases of complex wavelet transform and compounding information are more sensitive to singular points of signal than magnitudes, which can extract singularity of signal efficiently and position the singular points of signal accurately. Subsequently, the fault signal of automobile main reducer was analyzed by complex wavelet transform with its compounding information and real wavelet transform respectively. The results show that main reducer fault feature points can be positioned accurately by compounding information, and the decomposition just needs scale 1 calculation, which reduces the calculation greatly for fault feature extraction.