Development of a Virtual Sensor to Predict Cylinder Pressure Signal Based on a Knock Sensor Signal

Posch, Stefan; Pirker, Gerhard; Kefalas, Achilles; Wimmer, Andreas

doi:10.4271/2022-01-0627

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…In the study of Posch et al [7], results for the RMSE of PFP values of below 5 bar and the corresponding values for the RMSE of the PFP angular position of below 1.25 °CA were reported. By looking at Table 3, it can be seen that for the engine 1 test experiment, the RMSE value of PFP is 5.69 bar, whereas for the engine 2 test experiment, the obtained RMSE value of PFP is 3.7 bar.…”

Section: Discussionmentioning

confidence: 99%

“…The computational complexity of these models is reduced by an automatic learning of the nodes' splitting process, as well as a good performance in distributed computation [20]. Furthermore, the problem of overfitting was addressed by appending a regularization term Ω(θ) to the objective function within the learning procedure [39], as given in Equation (7).…”

Section: Discrete Wavelet Transform and Feature Extractionmentioning

confidence: 99%

“…In Equation (7), θ denotes the best set of parameters, whereas L(θ) is the training loss function and was defined as the commonly used mean squared error which is given in Equation ( 8) where y i are the labels and ŷi the predictions of the model.…”

Section: Discrete Wavelet Transform and Feature Extractionmentioning

confidence: 99%

“…The concept of virtual sensors can also be used to estimate combustion characteristics or even reconstruct the whole in-cylinder pressure from different source signals combined with various modelling approaches. In the study of Posch et al [7], it was shown that a successful correlation build up was achieved with the use of the KS signals in addition to the intake manifold pressure measurements combined in a simple differential equation. In Wang et al [8] such a virtual sensor was conceptualized by the use of extended Kalman filtering (EKF) and Frequency-Amplitude-Modulation Fourier series.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Discussionmentioning

confidence: 99%

Section: Discrete Wavelet Transform and Feature Extractionmentioning

confidence: 99%

Section: Discrete Wavelet Transform and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Misfire Detection Technology with Deep Neural Network Based on Ignition Coil Signals

Yoneya

Amaya

Kumano

et al. 2023

SAE Int. J. Engines

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<div>For achieving high efficiency and low exhaust emissions, engines need to be operated near the limits of stable combustion, such as lean or exhaust gas recirculation (EGR) conditions. Sensing technologies of the combustion state by existing engine components are of high interest. And the utilization of voltage and current signals from ignition coils is discussed in this article. The discharge channel of an ignition spark is strongly affected by flow variation and spark plug surface conditions, and the behavior of discharge channel stretching and restrike event can vary greatly from cycle to cycle. As a result, the effects of flow velocity, temperature, pressure, and electrode surface resistance are compounded in the voltage-current response, making it difficult to accurately detect the combustion state for each cycle by a threshold judgment process using a single feature value.</div> <div>In this article, a method for inductively detecting misfires from voltage and current signals of ignition coils by applying deep learning image recognition is introduced. First, post-ignition for misfire detection is performed on the engine bench during the expansion stroke in an engine cycle, when the cylinder pressure is expected to differ between the combustion cycle and the ignition cycle, and the ignition coil voltage and current are measured. Next, a two-dimensional frequency distribution of voltage and current (discharge histogram) is created as an input image for deep learning, and the AlexNet model, which has been trained with more than one million images, is trained with images of the ignition and combustion cycles as a supervised learning. The accuracy of classification is then verified using a validation dataset. In addition, to making the deep learning model more explainable, the activation score distribution on the discharge histogram was visualized when the trained model judges the images, and the discharge characteristics that provided the basis for deep learning classifications were analyzed.</div>

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A Comparison of Virtual Sensors for Combustion Parameter Prediction of Gas Engines Based on Knock Sensor Signals

Kefalas¹,

Ofner²,

Posch³

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction (MFB50) is essential for optimal engine control. These quantities are commonly determined from in-cylinder pressure sensor signals and are crucial to reach high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, alternative approaches which employ virtual sensing based on signals from non-intrusive sensors retrieved from common knock sensors are of great interest. This paper presents a comprehensive comparison of selected approaches from literature, as well as adjusted or further developed methods to determine engine combustion parameters based on knock sensor signals. All methods are evaluated on three different engines and two different sensor positions. The investigated approaches include a convolutional neural network, extreme gradient boosting regression models, non-linear feature regression models, a partial differential equation, as well as one method based on the analysis of structure-borne sound to derive an appropriate correlation. For evaluation of these implemented methods, data was acquired from extensive measurements of two spark-ignited single-cylinder large gas engines and one dual fuel single cylinder large engine under different operating conditions. The results show that the data-driven approaches achieved a root mean squared error (RMSE) of under 5.69 bar for the PFP and a RMSE of under 0.53 ° crank angle (CA) for the MFB50 across all investigated datasets. One method from the literature was adapted for the present study by applying the continuous wavelet transform and extracting certain features from the time-frequency spectrum to establish a suitable correlation for the desired combustion parameters. By achieving RMSE values for PFP of under 5.45 bar and for MFB50 of under 1.12 ° CA over all processed datasets, this adapted, novel method demonstrated high potential for the underlying regression tasks.</div></div>

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Development of a Virtual Sensor to Predict Cylinder Pressure Signal Based on a Knock Sensor Signal

Cited by 4 publications

References 13 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

Misfire Detection Technology with Deep Neural Network Based on Ignition Coil Signals

A Comparison of Virtual Sensors for Combustion Parameter Prediction of Gas Engines Based on Knock Sensor Signals

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