Misfire detection in spark-ignition engines with the EM algorithm

Villarino, R.; Böhme, J.F.

doi:10.1109/isspit.2003.1341080

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Another methodology that is being extensively investigated for misfire detection is the engine acoustic analysis. In this context, Villarino and Böhme [6] detected the misfire phenomenon using the expectation-maximization (EM) algorithm combined with a parametric pressure model to decompose the sound signal in individual components correlated with single combustions. Also, Boguś et al [7] led the research on nonlinear analysis of vibroacoustic signals intending the misfire diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Misfire detection of an internal combustion engine based on vibration and acoustic analysis

Firmino

Neto

Oliveira

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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A Misfire event is an unwanted phenomenon that compromises the regular operation of the engine, increasing the pollution of emission gases and decreasing its efficiency. Therefore, smart systems that detect misfire ensure better engine operation and compliance with government environmental regulations. This paper presents a comparative study of two smart systems that use two different methodologies for misfire detection in a 2006 Ford Zetec-Rocam engine, which is a 4-stroke sparkignition engine. The first methodology tested was vibration analysis, which consists of collecting vibration data from the engine block using a vibration acquisition system. After the acquisition stage, we extracted and selected the signal features using the fast Fourier transform (FFT) in order to recognize the fault patterns through the use of an artificial neural network (ANN), which presented an accuracy of 99.30%. The second methodology was the acoustic analysis of the engine sound. The data were collected by a sound acquisition system, and then the feature extraction and selection were done by using the same technique as we did in the vibration analysis. For this technique, the ANN developed presented an accuracy of 98.70%. Although the vibration analysis is slightly more accurate than the acoustic analysis for misfire diagnosis, the acoustic system has the advantage of not being necessary the physical contact between the data acquisition system and the engine. Therefore, both techniques may be applied with great accuracy, and the decision of using one approach or another will depend on the equipment availability and the user's specialty.

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Section: Introductionmentioning

confidence: 99%

Misfire detection of an internal combustion engine based on vibration and acoustic analysis

Firmino

Neto

Oliveira

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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show abstract

“…Other than recovering the in-cylinder pressure signal, system identification approach was also used for misfire detection [46][47][48]. A function was developed to interpret the ratio between the energy of the signal and the energy of the noise, termed as signal energy-to-noise ratio in Villarino's work.…”

Section: System Identification Frf Methods Assumed a Linear Dependencmentioning

confidence: 99%

Review of Sensing Methodologies for Estimation of Combustion Metrics

Jia

Naber

Blough

2016

Journal of Combustion

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For reduction of engine-out emissions and improvement of fuel economy, closed-loop control of the combustion process has been explored and documented by many researchers. In the closed-loop control, the engine control parameters are optimized according to the estimated instantaneous combustion metrics provided by the combustion sensing process. Combustion sensing process is primarily composed of two aspects: combustion response signal acquisition and response signal processing. As a number of different signals have been employed as the response signal and the signal processing techniques can be different, this paper did a review work concerning the two aspects: combustion response signals and signal processing techniques. In-cylinder pressure signal was not investigated as one of the response signals in this paper since it has been studied and documented in many publications and also due to its high cost and inconvenience in the application.

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“…Other than recovering the in-cylinder pressure signal, system identification approach was also used for misfire detection [64][65][66]. A function was developed to interpret the ratio between the energy of the signal and the energy of the noise, termed as signal energy to noise ratio in Villarino's work.…”

Section: System Identificationmentioning

confidence: 99%

Modeling of Transfer Path for Determination of Combustion and Noise Metrics on Diesel Engines

Jia¹

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Misfire detection in spark-ignition engines with the EM algorithm

Cited by 6 publications

References 5 publications

Misfire detection of an internal combustion engine based on vibration and acoustic analysis

Misfire detection of an internal combustion engine based on vibration and acoustic analysis

Review of Sensing Methodologies for Estimation of Combustion Metrics

Modeling of Transfer Path for Determination of Combustion and Noise Metrics on Diesel Engines

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