Internal Combustion Engine Noise Analysis With Time-Frequency Distribution

2002 ◽  
Vol 124 (3) ◽  
pp. 645-649 ◽  
Author(s):  
G. T. Zheng ◽  
A. Y. T. Leung
Keyword(s):  
Internal Combustion Engine ◽  
Frequency Distribution ◽  
Frequency Analysis ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Noise Signal ◽  
Engine Noise ◽  
Noise Sources ◽  
Time Frequency ◽  
Time Frequency Distribution

An analysis procedure, using the time-frequency distribution, has been developed for the analysis of internal combustion engine noise signals. It provides an approach making use of advantages of both the linear time-frequency distribution and the bilinear time-frequency distribution but avoiding their disadvantages. In order to identify requirements on the time-frequency analysis and also correlate a time-frequency analysis result with noise sources, the composition of the noise signal is discussed first. With this discussion, a mathematical model has been suggested for the noise signal. An example of identifying noise sources and detecting the abnormal condition of an injector with the noise signal time-frequency distribution for a diesel engine is also provided.

scholarly journals Noise Source Separation of an Internal Combustion Engine Based on a Single-Channel Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Jiachi Yao ◽  
Yang Xiang ◽  
Sichong Qian ◽  
Shuai Wang
Keyword(s):  
Internal Combustion Engine ◽  
Single Channel ◽  
Combustion Engine ◽  
Hot Spot ◽  
Internal Combustion ◽  
Combustion Noise ◽  
Channel Noise ◽  
Intrinsic Mode Functions ◽  
Noise Sources ◽  
Time Frequency

The separation and identification technology of noise sources is the focus and hot spot in the field of internal combustion engine noise research. Combustion noise and piston slap noise are the main noise sources of an internal combustion engine. However, both combustion noise and piston slap noise occur almost at the top dead center. They mix in the time domain and frequency domain. It is difficult to accurately and effectively separate them. A single-channel algorithm which combines time-varying filtering-based empirical mode decomposition (TVF-EMD) and robust independent component analysis (RobustICA) methods is proposed to separate them. Firstly, the TVF-EMD method is utilized to decompose the single-channel noise signal into several intrinsic mode functions (IMFs). Then, the RobustICA method is applied to extract the independent components. Finally, related prior knowledge and time-frequency analysis are employed to identify noise sources. Furthermore, the spectral filtering method and the calculation method of piston slap noise based on the dynamic model are further carried out to verify separation results. The simulation and experimental research results show the effectiveness of the proposed method.

Internal combustion engine noise reduction plate

1989 ◽  
Vol 85 (4) ◽  
pp. 1809-1809
Author(s):  
Benny Ballheimer ◽  
Michael K. Stratton
Keyword(s):  
Internal Combustion Engine ◽  
Noise Reduction ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Engine Noise

The Time-Frequency Analysis Method Based on EMD White Noise Energy Density Distribution Characteristics of the Internal Combustion Engine Vibration

2013 ◽  
Vol 328 ◽  
pp. 367-375 ◽  
Author(s):  
Guo Yan Feng ◽  
Yan Ping Cai ◽  
Yan Ping He
Keyword(s):  
Energy Density ◽  
Internal Combustion Engine ◽  
White Noise ◽  
Combustion Engine ◽  
Vibration Signal ◽  
Internal Combustion ◽  
Distribution Characteristics ◽  
Energy Density Distribution ◽  
Time Frequency ◽  
Engine Vibration

For the limitations of HHT of the internal combustion engine vibration signal analysis, and the problem of WVD cross-term suppression methods existing aggregation and cross-term component suppression conflicting, the time-frequency analysis method based on EMD white noise energy density distribution characteristics of the internal combustion engine vibration is proposed. First, the internal combustion engine vibration signal was decomposed into the independent series intrinsic mode function (IMF) with different characteristic time scales by using EMD decomposition method. Then, based on the energy density distribution characteristics of the white noise in EMD decomposition, used the distribution interval estimation curve of the IMFs energy density logarithm of white noise with the same length of the original signal as cordon for false pattern component, identified and eliminated false mode component of vibration signal IMFs component, analysised of each IMF with Wigner-Ville. Finally, the Wigner-Ville analysis results of each IMF were linear superposed in order to reconstruct the original signal time-frequency distribution. Simulation and engine vibration time-frequency analysis results show that this method has an excellent time-frequency characteristics, and can successfully extract feature information of the internal combustion engine cylinder head vibration signal.

scholarly journals Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Jiachi Yao ◽  
Yang Xiang ◽  
Sichong Qian ◽  
Shuai Wang
Keyword(s):  
Internal Combustion Engine ◽  
Filter Bank ◽  
Noise Source ◽  
Combustion Engine ◽  
Near Field ◽  
Source Separation ◽  
Internal Combustion ◽  
Combustion Noise ◽  
Time Frequency ◽  
Separation Results

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.

Sign in / Sign up

Export Citation Format

Share Document