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

Abstract

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.

Noise Source Identification of the Carpet Tufting Machine Based on the Single Channel Blind Source Separation and Time-Frequency Signal Analysis

“…e main contribution of this paper is the single channel blind source separation method based on EEMD-AIC-FastICA, and this method is applied in the noise source separation of the carpet tufting machine. In the literature review of Section 1, references [5][6][7] used EEMD-Robus-tICA, VMD-RobustICA, and TVF-EMD-RobustICA to separate the noise sources of the engine noise, respectively. ey all used an adaptive mode decomposition method.…”

“…Bi et al [5] used ensemble empirical mode decomposition and robust independent component analysis (EEMD-RobustICA) to separate and identify the noise sources of gasoline engines. Yao et al [6,7] separate and identify the noise sources of a diesel engine by using variational mode decomposition and robust independent component analysis (VMD-RobustICA) and time-varying filtering-based empirical mode decomposition and robust independent component analysis (TVF-EMD-RobustICA) respectively. e SCBSS methods in reference [4][5][6][7] all used the adaptive mode decomposition methods first, which are the EMD method, the EEMD method, the VMD method and the TVF-EMD method, and then some typical blind source separation methods were applied.…”

“…Yao et al [6,7] separate and identify the noise sources of a diesel engine by using variational mode decomposition and robust independent component analysis (VMD-RobustICA) and time-varying filtering-based empirical mode decomposition and robust independent component analysis (TVF-EMD-RobustICA) respectively. e SCBSS methods in reference [4][5][6][7] all used the adaptive mode decomposition methods first, which are the EMD method, the EEMD method, the VMD method and the TVF-EMD method, and then some typical blind source separation methods were applied. e reason of using the adaptive mode decomposition methods is to decompose the single channel signal into virtual multichannel signals.…”

“…e reason of using the adaptive mode decomposition methods is to decompose the single channel signal into virtual multichannel signals. After using the adaptive mode decomposition methods, the number of virtual multichannel signals was unknown and the subjective prior knowledge was used for determining the number in reference [4][5][6][7]. erefore, to overcome the problems above, a source number estimation method should be applied after using the adaptive mode decomposition methods.…”

“…e proposed EEMD-AIC-FastICA separation method not only solved the problem of unknown source number in reference [4][5][6][7], but also overcame the problem of non-independent source signals. An acquired single channel noise signal of the carpet tufting machine was separated into several independent components (ICs) by using the EEMD-AIC-FastICA method.…”