Source identification of gasoline engine noise based on continuous wavelet transform and EEMD–RobustICA

Bi, Fengrong; Li, Lin; Zhang, Jian; Ma, Teng

doi:10.1016/j.apacoust.2015.07.007

Cited by 42 publications

(40 citation statements)

References 15 publications

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“…For example, Zhang et al [11] adopted the ensemble empirical mode decomposition, the coherent power spectrum analysis, and the 2 Shock and Vibration improved analytic hierarchy process to separate and identify the noise sources of the diesel engine. Bi et al [12] utilised the EEMD-RobustICA method to separate the combustion noise, piston slap noise, and exhaust noise of the gasoline engine.…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Xiang

Qian

et al. 2017

Shock and Vibration

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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.

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Section: Introductionmentioning

confidence: 99%

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

Yao

Xiang

Qian

et al. 2017

Shock and Vibration

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“…Zhang et al [15] and Bi et al [16] utilized the empirical mode decomposition (EMD) and frequency modulated empirical mode decomposition (FM-EMD) to pre-process the vibration signals of a gearbox with different faults in diagnosing and monitoring the conditions of the gearbox. In addition, ensemble empirical mode decomposition (EEMD) and other methods were used to pre-process the vibration signals [17] and [18].…”

Section: Fault Diagnosis Methods Based On Modified Multiscale Entropy mentioning

confidence: 99%

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Li¹,

Wang²,

Zhao³

et al. 2019

SV-JME

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According to the nonlinearity, non-stationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, a fault diagnosis method of a reciprocating compressor valve based on modified multiscale entropy (MMSE) and global distance evaluation (GDE) is proposed. First, the variational mode decomposition (VMD) method with superior anti-interference performance was utilized to analyse the strong non-stationarity vibration signals for all fault states. The modified multiscale entropy (MMSE) method provided for movingaverage procedures by replacing mean-average coarse-grained procedures was developed for the vibration signals after de-noising, and then the GDE method of overall parameter selection was introduced to evaluate the extracted MMSE and to select the optimal sensitivity scale feature. Finally, a binary tree of support vector machine (BTSVM) was selected as the classifier to identify the reciprocating compressor valve fault type. By analysing the experimental data, it can be shown that the method can effectively identify the fault type of the reciprocating compressor valve. Keywords: fault diagnosis, reciprocating compressor valve, modified multiscale entropy, global distance evaluation, binary tree of support vector machine Highlights • An integrated fault feature extraction method of a reciprocating compressor valve based on the VMD-MMSE and GDE is proposed. • A consistent number K of band-limited intrinsic mode functions (BLIMFs) were selected based on a novel criterion for all fault states • The MMSE method provided for the moving-average procedure by replacing mean-average coarse-grained procedure was developed for the vibration signals, GDE was introduced to refine the eigenvectors for higher recognition efficiency and accuracy. • The effectiveness of this method is verified by the recognition results of BTSVM in comparison to other feature extraction methods.

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“…e scale selection method is utilized to select the IMFs which have the high coherence with noise source of the internal combustion engine for further calculation. e calculation expression of the correlation coefficient is shown in the following equation [15]:…”

Section: Separation and Identification Of Noise Sourcesmentioning

confidence: 99%

“…For example, Du et al [14] used the EMD and independent component analysis methods to separate the combustion excitation signal and piston slap signal. Bi et al [15] employed the EEMD-RobustICA method to separate the combustion noise, piston slap noise, and exhaust noise of gasoline engines. Zhang et al [16] utilized the EEMD, coherent power spectrum analysis, and improved analytic hierarchy process method to identify the noise sources of internal combustion engines.…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Xiang

Qian

et al. 2019

Shock and Vibration

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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.

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Source identification of gasoline engine noise based on continuous wavelet transform and EEMD–RobustICA

Cited by 42 publications

References 15 publications

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

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

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

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

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