独立分量分析在内燃机噪声信号分离中的应用

采用独立分量分析的方法进行了内燃机噪声信号分离的研究．建立了基于FastICA算法的常规内燃机噪声独立分量分析模型,为了减少所需传声器个数,在此基础上应用了时序独立分量分析模型．以某四缸柴油机为研究对象,测量了不同工况下的噪声信号,计算了这些噪声信号的统计峰度,确认其为非高斯信号,满足独立分量分析的基本要求．对测得的柴油机噪声信号进行了时序独立分量分析,将其分解为一系列不同的独立分量．采用小波变换的方法对它们进行分析,得到了各独立分量的时频分布,研究结果表明,这些独立分量对应着不同的内燃机噪声源信号．

Application of Independent Component Analysis to Decomposition of Engine Acoustic Signals

Independent component analysis (ICA) was carried out to decompose engine noise signals. Based on FastICA algorithm, the conventional ICA model for engine noise was set up, and further, the sequential independent component analysis model was applied. Take a 4-cylinder diesel engine for example, the recorded acoustic signals were computed for kurtosis, and found to be non-Gaussian. So the acoustic signals were then decomposed into several independent components by sequential independent component analysis. And the wavelet transform was applied to these components to get their time-frequency information. It was shown that these independent components were the approximation of different engine noise source signals.