Abstract
In the internal combustion engine noise source separation process, the combustion noise and the piston slap noise are found to be seriously aliased in time-frequency domain. It is difficult to accurately separate them. Therefore, the noise source separation method which is based on Gammatone filter bank and robust independent component analysis (RobustICA) is proposed. The 6-cylinder internal combustion engine vibration and noise test are carried out in a semianechoic chamber. The lead covering method is adopted to isolate the interference noise from numbers 1 to 5 cylinder parts, with only the number 6 cylinder parts left bare. Firstly, many mode components of the measured near-field radiated noise signals are extracted through the designed Gammatone filter bank. Then, the RobustICA algorithm is utilised to extract the independent components. Finally, the spectrum analysis, the continuous wavelet time-frequency analysis, the correlation function method, and the drag test are employed to further identify the separation results. The research results show that the frequency of the combustion noise and the piston slap noise are, respectively, concentrated at 4025 Hz and 1725 Hz. Compared with the EWT-RobustICA method, the separation results obtained by the Gammatone-RobustICA method have very fewer interference components.
Highlights
The internal combustion engine as power heart is widely used in ships, vehicles, and other means of transportation
According to the source of the internal combustion engine, the noise can be divided into mechanical noise, combustion noise, and aerodynamic noise [2,3,4]
In order to illustrate the performance of the GammatoneRobustICA method, some typical signals are selected to carry out the simulation analysis by MATLAB
Summary
The internal combustion engine as power heart is widely used in ships, vehicles, and other means of transportation. When the above single channel method is employed to separate and identify the noise source of the internal combustion engine, the first step is to decompose the single channel noise signal by the EEMD algorithm. Yao et al [18] utilised variational mode decomposition and robust independent component analysis to separate the noise source of diesel engine. These methods are widely used in bearing fault diagnosis [19,20,21]. There are many scholars establishing the computational auditory scene analysis model and algorithm based on the human ear hearing system to separate and identify the mixed speech signals [22].
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