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dc.contributor.authorKuzhagaliyeva, Nursulu
dc.contributor.authorThabet, Ali
dc.contributor.authorSingh, Eshan
dc.contributor.authorGhanem, Bernard
dc.contributor.authorSarathy, Mani
dc.date.accessioned2020-11-17T11:20:02Z
dc.date.available2020-11-17T11:20:02Z
dc.date.issued2020-11-12
dc.date.submitted2019-11-07
dc.identifier.citationKuzhagaliyeva, N., Thabet, A., Singh, E., Ghanem, B., & Sarathy, S. M. (2020). Using deep neural networks to diagnose engine pre-ignition. Proceedings of the Combustion Institute. doi:10.1016/j.proci.2020.10.001
dc.identifier.issn1540-7489
dc.identifier.doi10.1016/j.proci.2020.10.001
dc.identifier.urihttp://hdl.handle.net/10754/665996
dc.description.abstractEngine downsizing and boosting have been recognized as effective strategies for improving engine efficiency. However, operating the engines at high load promotes abnormal combustion events, such as pre-ignition and potential superknock. Currently the most effective method for detecting pre-ignition is by using in-cylinder pressure sensors that have high precision and sensitivity, but also high cost. Due to rapid advances in automotive technology such as autonomous driving, computer-aided designs and future connectivity, we propose to use a complimentary data-driven strategy for diagnosing abnormal combustion events. To this end, a data-driven diagnostics approach for pre-ignition detection with deep neural networks is proposed. The success of convolutional neural networks (CNNs) in object detection and recurrent neural networks (RNNs) in sequence forecasting inspired us to develop these models for pre-ignition detection. For a cost-effective strategy, we use data from less expensive sensors, such as lambda and low-resolution exhaust back pressure (EBP), instead of high resolution in-cylinder pressure measurements. The first deep learning model is combined with a commonly used dimensionality reduction tool–Principal Component Analysis (PCA). The second model eliminates this step and directly processes time-series data. Results indicate that the first model with reduced input dimensions, and correspondingly smaller size of the network, shows better performance in detecting pre-ignition cycles with an F1 score of 79%. Overall, the proposed deep learning approach is a promising alternative for abnormal combustion diagnostics using data from low resolution sensors.
dc.description.sponsorshipThis work was supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under the award number OSR-2019-CRG7-4077.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S1540748920306921
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Proceedings of the Combustion Institute. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Proceedings of the Combustion Institute, [, , (2020-11-12)] DOI: 10.1016/j.proci.2020.10.001 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleUsing deep neural networks to diagnose engine pre-ignition
dc.typeArticle
dc.contributor.departmentChemical Engineering
dc.contributor.departmentChemical Engineering Program
dc.contributor.departmentClean Combustion Research Center
dc.contributor.departmentCombustion and Pyrolysis Chemistry (CPC) Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentVCC Analytics Research Group
dc.identifier.journalProceedings of the Combustion Institute
dc.rights.embargodate2022-11-12
dc.eprint.versionPost-print
kaust.personKuzhagaliyeva, Nursulu
kaust.personSingh, Eshan
kaust.personGhanem, Bernard
kaust.personSarathy, Mani
kaust.grant.numberOSR-2019-CRG7-4077
dc.date.accepted2020-10-07
refterms.dateFOA2020-12-09T13:00:15Z
kaust.acknowledged.supportUnitOffice of Sponsored Research
kaust.acknowledged.supportUnitOSR
dc.date.published-online2020-11-12
dc.date.published-print2020-11


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