轿车底盘声信号分离算法研究  

Research on Sound Signal Separation Algorithm of the Car Chassis

作　　者：王骁 张恒嘉[1] 王薇 

机构地区：[1]中国汽车技术研究中心有限公司,天津300300

出　　处：《科技创新与应用》2019年第2期20-22,共3页Technology Innovation and Application

摘　　要：在诊断轿车底盘故障时,传统的时域与频域分析法对故障位置的判定有比较好的判断效果,但在局部缺陷的诊断时,这些判定方法却并不理想。这是由于底盘局部故障的存在很可能会产生瞬时突变信号,从而产生偶发性的冲击,使振动信号从平稳状况突变成非平稳状态。当故障特征信号比较弱时,传统的频谱方法检测难以达到一个好的效果,即对噪声的排除能力较弱,对细微的故障特征信号的识别能力不够灵敏,从而影响诊断的有效性和精确性。而小波分析法具有将信号局部特征识别出来的能力,通过很多的识别与处理工具的运用,使研究更深一步。文章基于底盘故障的诊断现状,深刻探讨了对基于小波的信号处理技术与特征识别提取的方法。In the diagnosis of car chassis faults,the traditional time-domain and frequency-domain analysis methods have a good effect on fault location,but in the diagnosis of local defects,these methods are not ideal.This is due to the fact that the existence of the chassis local fault is likely to lead to instantaneous sudden change signal,resulting in occasional impact,so that the vibration signal from a stable state to a non-stationary state.When the fault characteristic signal is weak,the traditional spectrum detection method is difficult to achieve a good effect,that is,the ability to eliminate noise is weak,and the ability to recognize the subtle fault characteristic signal is not enough,thus affecting the effectiveness and accuracy of diagnosis.Wavelet analysis has the ability to recognize the local features of the signal,and through the use of a lot of recognition and processing tools,further research can be conducted.Based on the present situation of chassis fault diagnosis,the signal processing technology based on wavelet and the method of feature recognition and extraction are deeply discussed in this paper.

关 键 词：底盘噪声 特征提取 小波图像 

分 类 号：U463[机械工程—车辆工程]