摘要
为了分离和识别内燃机噪声源,结合独立分量分析和小波变换技术对内燃机辐射噪声信号进行了盲源分离和声源识别的研究.根据独立分量分析的基本原理,采用基于负熵极大的FastICA算法对4缸柴油机的辐射噪声信号进行了盲源分离,将噪声信号分解成一系列独立分量.采用快速傅里叶变换和小波变换技术对各个独立分量进行了分析,结合时频分析的结果和内燃机各噪声源信号的频谱结构,确定了分离得到的各独立分量与内燃机不同噪声源的对应关系.研究结果表明:这些独立分量分别对应着柴油机的燃烧噪声、活塞敲击噪声、正时齿轮噪声及排气辐射噪声等噪声源.
Independent component analysis（ICA）and wavelet transform technology were used to study the blind source separation and noise source identification of engine radiation noise.Based on independent component analysis theory,the FastICA algorithm to get maximum negative entropy was adopted to separate noise signals of the four cylinder diesel engine,and a series of independent components were obtained.Fast Fourier transform and wavelet transform were used to identify the corresponding relationship between different engine noise sources and independent components.Combining the time-frequency analysis results and engine noise sources frequency spectrums,the corresponding relationship was determined.Results show that these independent components correspond to diesel engine combustion noise,piston knocking noise,timing gear noise and exhaust noise respectively.
出处
《内燃机学报》
EI
CAS
CSCD
北大核心
2012年第2期166-171,共6页
Transactions of Csice
基金
天津市应用基础及前沿技术研究计划资助项目(10JCZDJC23200)
国家自然科学基金资助项目(50975192)
关键词
内燃机
盲源分离
噪声识别
独立分量分析
小波变换
internal combustion engine
blind source separation
noise identification
independent component analysis
wavelet transform
作者简介
王霞，博士研究生，E-mail：wangxia@tju．edu．cn．
通讯作者：毕凤荣，副教授，博士，E-mail：fr_bi@tjuedu．cn．
