Failure Prediction of Highly Requested Complex Technical Systems: Application to W18v50df Engines

Offole, Florence; Essola, Dieudonné; Issondj, Nelson; Bouheul, Charly

doi:10.31695/ijasre.2020.33798

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Indeed, the optimization of a heat engine can be considered under three main aspects: the architectural aspect aimed at modifying the geometry of the engine in order to optimize its operation, the phenomenological aspect aimed at modifying the elements involved in combustion in order to optimize and the control aspect which aims to develop algorithms for optimal engine management. Due to the technological advances of the last decades, the acquisition of massive operating data of thermal engines has become possible, something which has facilitated enrichment in the fields of artificial intelligence in the era of the democratization of microprocessors and embedded systems [ 4]. Thus, the use of such systems for modeling and / or controlling thermal engines is highly desirable.…”

Section: Introductionmentioning

confidence: 99%

Control-Oriented Modeling Of A Thermal Engine Based On A Deep Learning Approach

Banta

Offole

Essola

et al. 2021

Preprint

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The work carried out in this paper focused on "control-oriented modeling of a thermal engine using a deep learning approach". The goal of this work is to develop a neural network model of a thermal engine and to make a prediction of parameters related to engine management and directly impacting pollutant emissions and fuel consumption. For it, data was taken from an experimentation engine and made it possible to make maps of its operation. These maps enabled the calibration of a Simulink model of a thermal engine. Through a systems identification approach, the temporal response of the motor was estimated and made it possible to develop a database which was used for the training the LSTM artificial neural network. The work carried out showed that the learning phase of the neural network proceeded in a consistent way (overall decrease in cost functions) and converged towards a value of RMSE = 1.09 better than those observed in the literature. The resulting neural engine model made it possible to predict several variables (fuel mass flow rate and pollutant mass flow rates) with acceptable residual errors. These results reveal that the neural model obtained correctly predicts the said variables and can therefore be used in closed-loop simulations of the operation of a vehicle or for a context of simulation of the operation of the engine.

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Section: Introductionmentioning

confidence: 99%

Control-Oriented Modeling Of A Thermal Engine Based On A Deep Learning Approach

Banta

Offole

Essola

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the technological advances of the last decades, the acquisition of massive operating data of thermal engines has become possible, something which has facilitated enrichment in the fields of artificial intelligence in the era of the democratization of microprocessors and embedded systems [6]. Thus, the use of such systems for modeling and/or controlling thermal engines is highly desirable.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Performance and Emissions Related Parameters in a Thermal Engine Using a Deep Learning Approach

et al. 2021

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The work carried out in this paper focused on "Simulation of Performance and Emissions Related Parameters in A Thermal Engine using A Deep Learning Approach". The goal of this work is to develop a neural network model of a thermal engine and to make a prediction of parameters related to engine management and directly impacting pollutant emissions and fuel consumption. The novelty of this work is the use of a particular type of neural network to learn long sequence of data, obtained from a running engine, and to predict internal parameters related to engine performance and emissions with a good precision. For it, data were taken from an experimentation engine, the L15B7 1.5 L, a gasoline engine with direct lateral injection from the manufacturer Honda, fitted to Honda Civic vehicles. These data made it possible to make maps of its operation. These maps enabled the calibration of a Simulink model of a thermal engine. Through a system identification approach, the temporal response of the motor was estimated and made it possible to develop a database that was used for training the LSTM artificial neural network. The work carried out showed that the learning phase of the neural network proceeded consistently (overall decrease in cost functions) and converged towards a value of RMSE = 1.09 better than those observed in the literature. The resulting neural engine model made it possible to predict several variables (fuel mass flow rate and pollutant mass flow rates) with acceptable residual errors. These results reveal that the neural model obtained correctly predicts the said variables and can, therefore, be used in closed-loop simulations of the operation of a vehicle or for a context of simulation of the operation of the engine.

show abstract

Failure Prediction of Highly Requested Complex Technical Systems: Application to W18v50df Engines

Cited by 2 publications

References 19 publications

Control-Oriented Modeling Of A Thermal Engine Based On A Deep Learning Approach

Control-Oriented Modeling Of A Thermal Engine Based On A Deep Learning Approach

Simulation of Performance and Emissions Related Parameters in a Thermal Engine Using a Deep Learning Approach

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