Pressure reconstruction and misfire detection from multichannel structure-borne sound

Villarino, R.; Böhme, J.F.

doi:10.1109/icassp.2004.1326214

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…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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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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“…That is, to calculate the indicated mean effective pressure by measuring the pressure in engine cylinders and to compare it with that of the normal condition (Villarino and Bohme, 2004), then the fault can be distinguished from the result. Detecting the fault by the oxygen was contained in the exhaust gas.…”

Section: The Selection Of Sampling Frequency and Pointsmentioning

confidence: 99%

Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software

Hebiel¹,

Zhu²,

Wang³

et al. 2014

RJASET

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Experiment presented in this study, used vibration data obtained from a four-stroke, 295 diesel engine. Fault of the internal-combustion engine was detected by using the vibration signals of the cylinder head. The fault diagnosis system was designed and constructed for inspecting the status and fault diagnosis of a diesel engine based on discrete wavelet analysis and LabVIEW software. The cylinder-head vibration signals were captured through a piezoelectric acceleration sensor, that was attached to a surface of the cylinder head of the engine, while the engine was running at two speeds (620 and 1300 rpm) and two loads (15 and 45 N•m). Data was gathered from five different conditions, associated with the cylinder head such as single cylinder shortage, double cylinders shortage, intake manifold obstruction, exhaust manifold obstruction and normal condition. After decomposing the vibration signals into some of the details and approximations coefficients with db5 mother wavelet and decomposition level 5, the energies were extracted from each frequency sub-band of healthy and unhealthy conditions as a feature of engine fault diagnosis. By doing so, normal and abnormal conditions behavior could be effectively distinguished by comparing the energy accumulations of each sub-band. The results showed that detection of fault by discrete wavelet analysis is practicable. Finally, two techniques, Back-Propagation Neural Network (BPNN) and Support Victor Machine (SVM) were applied to the signal that was collected from the diesel engine head. The experimental results showed that BPNN was more effective in fault diagnosis of the internal-combustion engine, with various fault conditions, than SVM.

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“…Recent efforts are developing the knowledge in how its signal relates to the combustion. Polonowski et al (2007) and Chiavola et al (2010) analyzed the coherence for different frequency bands between the accelerometer signal and the pressure based heat release in a diesel engine, and Villarino and Boehme (2005) have developed methods for reconstructing cylinder pressure and used it for misfire detection and peak pressure position estimation.…”

Section: Introductionmentioning

confidence: 99%

Detection of Combustion Properties in a Diesel Engine using Block Mounted Accelerometers

Andersson

McKelvey

Larsson

2014

IFAC Proceedings Volumes

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Engine block mounted accelerometers was used for estimating combustion phasing parameters for a heavy duty diesel engine. Several sensor locations were evaluated for its sensitivity to combustion related vibrations and mechanical noise. One sensor-cylinder combination was selected for the evaluation of a simple algorithm to detect 10% and 50% burned mass fraction. The algorithm uses the accumulated accelerometer vibration energy as a base for extracting the 10% and 50% points respectively. The results show that the angular positions for 10% and 50% burned mass fraction can be estimated with precisions of 1.5 and 3 CAD respectively. It is also concluded that the early part of the accelerometer signal has a significant influence from a mechanical noise source related to the start of injection.

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Pressure reconstruction and misfire detection from multichannel structure-borne sound

Cited by 10 publications

References 3 publications

Review of Sensing Methodologies for Estimation of Combustion Metrics

Review of Sensing Methodologies for Estimation of Combustion Metrics

Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software

Detection of Combustion Properties in a Diesel Engine using Block Mounted Accelerometers

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