汽车雨刮电机电磁干扰特征提取方法  

Electromagnetic interference feature extraction for vehicle wiper motor signals based on wavelet decomposition

作　　者：安宗裕[1] 汪泉弟[1] 彭河蒙[1] 

机构地区：[1]重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆400044

出　　处：《电机与控制学报》2012年第7期1-6,共6页Electric Machines and Control

基　　金：国家自然科学基金(51177183);中央高校基本科研业务费资助项目(CDJXS11150005);重庆大学研究生科技创新基金(CDJXS11150005)

摘　　要：针对汽车内部严重的电磁兼容问题,需要对车内电磁干扰源进行准确的分析。采用小波变换方法对汽车雨刮电机干扰信号进行多尺度分解,利用分解得到的各高频子带的小波系数特点,提出了小波域内突变参数和不平衡参数的计算方法,分并且提取各小波系数的累积能量参数、突变参数以及不平衡参数,并与重构后的各层细节信号的波形特征作了对比分析。结果表明,小波系数的累积能量参数、突变参数和不平衡参数能够准确描述原始信号中不同成分信号的特征,对电磁干扰源的识别和分类具有指导价值。此外,这种方法对其他电磁干扰信号的特征提取具有参考价值。Most of the existing techniques for recognizing and identifying the disturbance signal waveforms of a vehicle electrical system are primarily based on visual inspection. A wavelet decomposition-based technique to perform a feature extraction from the disturbance signal. The disturbance signal of the viper motor was first decomposed into various frequency sub-band signals, after which a method of calculating mutation and non-equilibrium parameters were presented using the wavelet coefficient of the signal after decomposition. Finally, the mutation and non - equilibrium parameters of the wavelet coefficient of the wiper motor disturbance signals were obtained, which are compared with the waveform character of the reconstructed signal. Based on the results, the extracted feature parameters of the wavelet coefficient of the disturbance signal can effectively represent the different composition of the original signal. Knowing the meaning and use for EMI （electromagnetic interference） source identification and classification using the proposed method is significant. The results are valuable for the feature extraction of other disturbance signals in automobiles.

关 键 词：汽车 雨刮电机 电磁干扰 小波变换 特征提取 

分 类 号：TM33[电气工程—电机]