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    Using deep neural networks to diagnose engine pre-ignition

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    Name:
    Using_deep_neural_networks_to_diagnose_engine_pre_ignition.pdf
    Size:
    1.239Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2022-11-12
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    Type
    Article
    Authors
    Kuzhagaliyeva, Nursulu cc
    Thabet, Ali
    Singh, Eshan cc
    Ghanem, Bernard cc
    Sarathy, Mani cc
    KAUST Department
    Chemical Engineering
    Chemical Engineering Program
    Clean Combustion Research Center
    Combustion and Pyrolysis Chemistry (CPC) Group
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    VCC Analytics Research Group
    KAUST Grant Number
    OSR-2019-CRG7-4077
    Date
    2020-11-12
    Online Publication Date
    2020-11-12
    Print Publication Date
    2020-11
    Embargo End Date
    2022-11-12
    Submitted Date
    2019-11-07
    Permanent link to this record
    http://hdl.handle.net/10754/665996
    
    Metadata
    Show full item record
    Abstract
    Engine 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.
    Citation
    Kuzhagaliyeva, 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
    Sponsors
    This work was supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under the award number OSR-2019-CRG7-4077.
    Publisher
    Elsevier BV
    Journal
    Proceedings of the Combustion Institute
    DOI
    10.1016/j.proci.2020.10.001
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S1540748920306921
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.proci.2020.10.001
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Electrical and Computer Engineering Program; Chemical Engineering Program; Mechanical Engineering Program; Clean Combustion Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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