Blind source separation and identification of internal combustion engine noise based on independent component and wavelet analysis

Abstract

The independent component analysis and wavelet transform technology are used to separate and identify the internal combustion engine noise signal. According to the basic principle of independent component analysis, FastICA based on negative entropy great with the good stability and convergence speed algorithm is applied to separate the noise signals of six cylinder diesel engine. And the noise signals are decomposed into a series of independent components. The fast Fourier transform and wavelet transform technology are applied on each independent component analysis. Combining with the time-frequency analysis results and the internal combustion engine noise signal spectrum and the structure of the separation of the independent component and getting the corresponding relationship of different internal combustion engine noise sources. The results show that: the independent components correspond to the diesel engine combustion noise sources, the piston knock noise, and fuel injection pump noise and so on.