Acoustic feature extraction for monitoring the combustion process of diesel engine based on EMD and wavelet analysis

Shunting Fang, Si Chang Li, Dong Zhen, Zhanqun Shi, Fengshou Gu, Andrew David Ball

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In order to analyze the combustion characteristic of the internal-combustion engine, combustion noise signal was collected, replacing the cylinder pressure signal in this paper. An acoustic signal feature extraction method using empirical mode decomposition (EMD) and wavelet analysis (WA) was proposed. Time synchronous average (TSA) is used for filtering interference noise to enhance the signal-to-noise ratio (SNR) of the measured acoustic signal as noise will affect the decomposition process resulting in over decomposition. The processed acoustic signals were decomposed into a series of intrinsic mode functions (IMF) using the method of empirical mode decomposition (EMD) in the time domain. Then wavelet analysis which has good time-frequency localization feature was applied on useful IMFs containing the information of diesel engine combustion. The root mean square (RMS) value of wavelet analysis results was calculated to achieve linear state monitoring.

LanguageEnglish
Pages25-30
Number of pages6
JournalInternational Journal of COMADEM
Volume20
Issue number3
Publication statusPublished - 1 Jul 2017

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Wavelet analysis
Diesel engines
Feature extraction
Acoustics
Decomposition
Monitoring
Acoustic noise
Engine cylinders
Internal combustion engines
Signal to noise ratio
Combustion
Diesel engine
Decomposition analysis

Cite this

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title = "Acoustic feature extraction for monitoring the combustion process of diesel engine based on EMD and wavelet analysis",
abstract = "In order to analyze the combustion characteristic of the internal-combustion engine, combustion noise signal was collected, replacing the cylinder pressure signal in this paper. An acoustic signal feature extraction method using empirical mode decomposition (EMD) and wavelet analysis (WA) was proposed. Time synchronous average (TSA) is used for filtering interference noise to enhance the signal-to-noise ratio (SNR) of the measured acoustic signal as noise will affect the decomposition process resulting in over decomposition. The processed acoustic signals were decomposed into a series of intrinsic mode functions (IMF) using the method of empirical mode decomposition (EMD) in the time domain. Then wavelet analysis which has good time-frequency localization feature was applied on useful IMFs containing the information of diesel engine combustion. The root mean square (RMS) value of wavelet analysis results was calculated to achieve linear state monitoring.",
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author = "Shunting Fang and Li, {Si Chang} and Dong Zhen and Zhanqun Shi and Fengshou Gu and Ball, {Andrew David}",
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Acoustic feature extraction for monitoring the combustion process of diesel engine based on EMD and wavelet analysis. / Fang, Shunting; Li, Si Chang; Zhen, Dong; Shi, Zhanqun; Gu, Fengshou; Ball, Andrew David.

In: International Journal of COMADEM, Vol. 20, No. 3, 01.07.2017, p. 25-30.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Acoustic feature extraction for monitoring the combustion process of diesel engine based on EMD and wavelet analysis

AU - Fang, Shunting

AU - Li, Si Chang

AU - Zhen, Dong

AU - Shi, Zhanqun

AU - Gu, Fengshou

AU - Ball, Andrew David

PY - 2017/7/1

Y1 - 2017/7/1

N2 - In order to analyze the combustion characteristic of the internal-combustion engine, combustion noise signal was collected, replacing the cylinder pressure signal in this paper. An acoustic signal feature extraction method using empirical mode decomposition (EMD) and wavelet analysis (WA) was proposed. Time synchronous average (TSA) is used for filtering interference noise to enhance the signal-to-noise ratio (SNR) of the measured acoustic signal as noise will affect the decomposition process resulting in over decomposition. The processed acoustic signals were decomposed into a series of intrinsic mode functions (IMF) using the method of empirical mode decomposition (EMD) in the time domain. Then wavelet analysis which has good time-frequency localization feature was applied on useful IMFs containing the information of diesel engine combustion. The root mean square (RMS) value of wavelet analysis results was calculated to achieve linear state monitoring.

AB - In order to analyze the combustion characteristic of the internal-combustion engine, combustion noise signal was collected, replacing the cylinder pressure signal in this paper. An acoustic signal feature extraction method using empirical mode decomposition (EMD) and wavelet analysis (WA) was proposed. Time synchronous average (TSA) is used for filtering interference noise to enhance the signal-to-noise ratio (SNR) of the measured acoustic signal as noise will affect the decomposition process resulting in over decomposition. The processed acoustic signals were decomposed into a series of intrinsic mode functions (IMF) using the method of empirical mode decomposition (EMD) in the time domain. Then wavelet analysis which has good time-frequency localization feature was applied on useful IMFs containing the information of diesel engine combustion. The root mean square (RMS) value of wavelet analysis results was calculated to achieve linear state monitoring.

KW - Diesel engine

KW - EMD

KW - Feature extraction

KW - Wavelet analysis

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