A Virtual Sensor for Predicting Diesel Engine Emissions from Cylinder Pressure Data
(2012) 2012 IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM'12) In IFAC Proceedings Volumes 45(30). p.424-431- Abstract
- Cylinder pressure sensors provide detailed information on the diesel engine combustion process. This paper presents a method to use cylinder-pressure data for prediction of engine emissions by exploiting data-mining techniques. The proposed method uses principal component analysis to reduce the dimension of the cylinder-pressure data, and a neural network to model the nonlinear relationship between the cylinder pressure and emissions. An algorithm is presented for training the neural network to predict cylinder-individual emissions even though the training data only provides cylinder-averaged target data. The algorithm was applied to an experimental data set from a six-cylinder heavy-duty engine, and it is verified that trends in emissions... (More)
- Cylinder pressure sensors provide detailed information on the diesel engine combustion process. This paper presents a method to use cylinder-pressure data for prediction of engine emissions by exploiting data-mining techniques. The proposed method uses principal component analysis to reduce the dimension of the cylinder-pressure data, and a neural network to model the nonlinear relationship between the cylinder pressure and emissions. An algorithm is presented for training the neural network to predict cylinder-individual emissions even though the training data only provides cylinder-averaged target data. The algorithm was applied to an experimental data set from a six-cylinder heavy-duty engine, and it is verified that trends in emissions during transient engine operation are captured successfully by the model. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3404642
- author
- Henningsson, Maria LU ; Tunestål, Per LU and Johansson, Rolf LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 3rd IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling
- series title
- IFAC Proceedings Volumes
- volume
- 45
- issue
- 30
- edition
- 20
- pages
- 8 pages
- conference name
- 2012 IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM'12)
- conference location
- Rueil-Malmaison, France
- conference dates
- 2012-10-23 - 2012-10-25
- external identifiers
-
- scopus:84881009518
- ISSN
- 1474-6670
- ISBN
- 978-3-902823-16-8
- DOI
- 10.3182/20121023-3-FR-4025.00063
- project
- Competence Centre for Combustion Processes
- Diesel HCCI in a Multi-Cylinder Engine
- language
- English
- LU publication?
- yes
- id
- 9b0d152b-e6ed-4ce2-8a68-e157d901c718 (old id 3404642)
- date added to LUP
- 2016-04-01 14:43:07
- date last changed
- 2024-06-07 09:27:40
@inproceedings{9b0d152b-e6ed-4ce2-8a68-e157d901c718, abstract = {{Cylinder pressure sensors provide detailed information on the diesel engine combustion process. This paper presents a method to use cylinder-pressure data for prediction of engine emissions by exploiting data-mining techniques. The proposed method uses principal component analysis to reduce the dimension of the cylinder-pressure data, and a neural network to model the nonlinear relationship between the cylinder pressure and emissions. An algorithm is presented for training the neural network to predict cylinder-individual emissions even though the training data only provides cylinder-averaged target data. The algorithm was applied to an experimental data set from a six-cylinder heavy-duty engine, and it is verified that trends in emissions during transient engine operation are captured successfully by the model.}}, author = {{Henningsson, Maria and Tunestål, Per and Johansson, Rolf}}, booktitle = {{3rd IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling}}, isbn = {{978-3-902823-16-8}}, issn = {{1474-6670}}, language = {{eng}}, number = {{30}}, pages = {{424--431}}, series = {{IFAC Proceedings Volumes}}, title = {{A Virtual Sensor for Predicting Diesel Engine Emissions from Cylinder Pressure Data}}, url = {{https://lup.lub.lu.se/search/files/4124678/3806421.pdf}}, doi = {{10.3182/20121023-3-FR-4025.00063}}, volume = {{45}}, year = {{2012}}, }