Efficient Point-by-point Engine Calibration using Machine Learning and Sequential Design of Experiment Strategies

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Status	已發表Published
	Efficient Point-by-point Engine Calibration using Machine Learning and Sequential Design of Experiment Strategies
	Wong, P. K.; Gao, X.H.; Wong, K.I.; Vong, C. M.
	2018-03-01
Source Publication	Journal of the Franklin Institute - Engineering and Applied Mathematics (SCI-E)
ISSN	0016-0032
Pages	1517-1538
Abstract	Modern engines are controlled by electronic control units (ECUs), which operate all the engine actuators based on the signals from various sensors in the engine. The control parameters of the actuators are stored in the form of look-up tables in ECUs. Traditionally, these parameters are obtained through huge amount of trial-and-error experiments. However, using traditional approach to calibrate these parameters becomes more challenging with the increasing incorporation of new technologies into advanced engines. In order to reduce the number of experiments required in the calibration process of modern engines, a novel point-by-point engine calibration approach based on machine learning methods is proposed in this study. It is an iterative procedure that, for a given operating point, sequential design of experiment (DoE) strategy is utilized to measure the responses of different engine sensors corresponding to different actuator signals, and a machine learning algorithm called initial-training-free online extreme learning machine (ITF-OELM) is utilized to incrementally learn the relationship between the sensors and actuators of the engine based on the measurement acquired. In each iterative cycle, meta-heuristic optimization is performed on the machine-learning-based model to search for the best parameters, which are then used as the initial parameters for generating DoE plan of the next cycle. The iteration is repeated until the optimal parameters of that operating point are found. To verify the effectiveness of the proposed approach, experiments on both simulation engine in commercial software and real engine in test bench have been conducted. The results show that the engine calibration can be carried out with significant fewer experiments and time by using the proposed approach.
Keyword	Engine calibration engine modeling data processing optimization sequential design of experiment online extreme learning machine
Language	英語English
The Source to Article	PB_Publication
PUB ID	28751
Document Type	Journal article
Collection	Faculty of Science and Technology
Corresponding Author	Wong, P. K.
Recommended Citation GB/T 7714	Wong, P. K.,Gao, X.H.,Wong, K.I.,et al. Efficient Point-by-point Engine Calibration using Machine Learning and Sequential Design of Experiment Strategies[J]. Journal of the Franklin Institute - Engineering and Applied Mathematics (SCI-E), 2018, 1517-1538.
APA	Wong, P. K.., Gao, X.H.., Wong, K.I.., & Vong, C. M. (2018). Efficient Point-by-point Engine Calibration using Machine Learning and Sequential Design of Experiment Strategies. Journal of the Franklin Institute - Engineering and Applied Mathematics (SCI-E), 1517-1538.
MLA	Wong, P. K.,et al."Efficient Point-by-point Engine Calibration using Machine Learning and Sequential Design of Experiment Strategies".Journal of the Franklin Institute - Engineering and Applied Mathematics (SCI-E) (2018):1517-1538.

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