Review of Sensing Methodologies for Estimation of Combustion Metrics

Jia, Libin; Naber, Jeffrey; Blough, Jason R.

doi:10.1155/2016/8593523

Cited by 15 publications

(5 citation statements)

References 48 publications

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“…Since combustion events introduce non-stationary effects within the signals recorded by the KS, DWT is more suitable than classic Fourier analysis due to revealed information in the time and frequency domain [16]. In initial experiments, several mother wavelets were tested and it was discovered that, for the present experimental settings, the Haar wavelets performed best.…”

Section: Discrete Wavelet Transform and Feature Extractionmentioning

confidence: 99%

“…Discrete wavelet transform (DWT) is a multi-resolution decomposition and allows feature analysis associated with different frequency bands [16]. The received coefficients are resolved in the time and frequency domains and are therefore suitable for the analysis of non-stationary phenomena, as they are expected to be introduced by the complex combustion processes of an engine [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

Kefalas

Ofner

Pirker³

et al. 2022

Sensors

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An optimal control of the combustion process of an engine ensures lower emissions and fuel consumption plus high efficiencies. Combustion parameters such as the peak firing pressure (PFP) and the crank angle (CA) corresponding to 50% of mass fraction burned (MFB50) are essential for a closed-loop control strategy. These parameters are based on the measured in-cylinder pressure that is typically gained by intrusive pressure sensors (PSs). These are costly and their durability is uncertain. To overcome these issues, the potential of using a virtual sensor based on the vibration signals acquired by a knock sensor (KS) for control of the combustion process is investigated. The present work introduces a data-driven approach where a signal-processing technique, designated as discrete wavelet transform (DWT), will be used as the preprocessing step for extracting informative features to perform regression tasks of the selected combustion parameters with extreme gradient boosting (XGBoost) regression models. The presented methodology will be applied to data from two different spark-ignited, single cylinder gas engines. Finally, an analysis is obtained where the important features based on the model’s decisions are identified.

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Section: Discrete Wavelet Transform and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

Kefalas

Ofner

Pirker³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Morello et al [39] analyzed crankshaft vibration to locate slapping motion of the skirt and combustion noise. In Jia et al [40] concluded that the accelerometer signals were significantly influenced from a mechanical noise sources due to the start of injection. Research into the estimation of diesel engine combustion metrics via remote combustion sensing has become an increasingly active area of combustion research [41].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Costs of vibration and noise control are usually very high, e.g., the yearly costs of warranty for brake was about 1 Billion U.S. $. during the year 2005 [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. So it is necessary to focus on NVH aspects of combustion engines.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

Narayan¹,

Kaisan²,

Abubakar³

et al. 2021

Sound&Vibration

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The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection parameters was analyzed. Scope of using non-intrusive technique has been analyzed by changing locations of microphone. Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up. Various frequency ranges of contributing noise and vibration sources were identified. Time-Frequency analysis showed the onset of various cyclic. Based on the identification of various frequency bands, it is possible to device suitable filters in order to extract more information.

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“…At each operating point, a total of 1002 cycles of data were collected. In common with many previous studies, 23,24 this data included data recorded from flush-mounted pressure sensors installed in each cylinder as well as two production accelerometer-type knock sensors mounted on the engine, one on each bank. A high-quality encoder was used to sample the data every 1/10th of a crank angle degree, together with the measured time interval between samples.…”

Section: Data Collection and Initial Analysismentioning

confidence: 99%

The statistical characteristics of knock signal waveforms

Jones

Patel

2021

International Journal of Engine Research

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Individual instances of the knock resonant response are easy to acquire but these are subject to noise and vary considerably from cycle to cycle due to random variations in the knock process. This work provides a new way to quantify and model the stochastic properties of knock signals, capturing both the time domain resonant characteristics within a cycle as well as the random variations from cycle to cycle. A new phase alignment method enables the ensemble mean knock waveform to be identified from the data which also removes noise components without the need for narrowband filtering. This ensemble waveform shows the empirical characteristics of knock onset, decay, and frequency slurring within the cycle as the gas expands and cools. The phase-aligned cyclic variations of the knock waveform are also shown to approximate a (time-varying) dual-Gaussian distribution, and fitting such a model to the data enables the statistical properties of the dataset as a whole to be decomposed into separate knocking and non-knocking populations providing further insight into the knock process. The technique is applied both to filtered cylinder pressure signals and to accelerometer-based knock signals, and the results are compared and contrasted.

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Review of Sensing Methodologies for Estimation of Combustion Metrics

Cited by 15 publications

References 48 publications

Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

The statistical characteristics of knock signal waveforms

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