基于小波变换的非平稳排气噪声信号阶次分析方法

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摘要汽车排气阶次噪声提取对于汽车声品质以及汽车分类识别具有重要的意义。为准确提取阶次噪声的时域信号，本文提出一种基于小波变换的非线性多分辨率的细化分析计算方法。通过理想带通传递函数构建基小波函数，基小波函数通过平移和伸缩实现信号的局部细化分析。探索小波函数的截取方式对频谱泄漏的影响，选取合适的窗函数截取小波函数，并通过与时域噪声信号的相关变换准确提取各阶次成分的时域波动信号。最后，通过实车测试获取加速工况下的转速脉冲信号和排气辐射噪声信号，对以上方法进行验证。结果表明，本文提出的分析方法可准确提取出非平稳排气噪声中的阶次成分，为排气噪声阶次分析提供标准可靠的信号处理手段。

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	刘海涛

关键词 ：非平稳时变信号, 阶次, 基小波函数, 窗函数, 相关变换

Abstract：Vehicle exhaust order noise extraction is of great significance for vehicle sound quality research and vehicle classification and recognition. In order to extract the time domain signal of exhaust order noise, a nonlinear refining analysis method with multi-resolution based on wavelet transform was proposed. The basis wavelet function was constructed by ideal band-pass transfer function. The local refinement analysis of signals was realized by translation and scaling of the basis wavelet function. The influence of the interception method of wavelet function on the spectrum leakage was explored. A proper window function was selected to intercept wavelet function, which was used to accurately extract the time domain signal of each order components through the correlation transform with the time domain noise signal. At last, the speed pulse signal and the exhaust noise signal under the accelerating condition were obtained by real vehicle test, and the analysis method of this work was verified. The results show that the analysis method proposed in this paper can accurately extract the order components in the non-stationary exhaust noise, thus providing a reliable and standard signal processing method for the order analysis of exhaust noise.

Key words： Non-stationary time-varying signals order basis wavelet function window function correlation transform

收稿日期: 2018-06-26 出版日期: 2019-11-15

引用本文:

刘海涛 . 基于小波变换的非平稳排气噪声信号阶次分析方法[J]. 振动与冲击, 2019, 38(22): 29-35.
LIU Haitao. Order Analysis of Non-stationary Exhaust Noise Signals based on Wavelet Transform. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(22): 29-35.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I22/29

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