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
Type
ArticleKAUST Department
Chemical EngineeringChemical 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-CRG7-4077Date
2020-11-12Online Publication Date
2020-11-12Print Publication Date
2020-11Embargo End Date
2022-11-12Submitted Date
2019-11-07Permanent link to this record
http://hdl.handle.net/10754/665996
Metadata
Show full item recordAbstract
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.2020.10.001Sponsors
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 BVAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S1540748920306921ae974a485f413a2113503eed53cd6c53
10.1016/j.proci.2020.10.001