Misfire detection of a turbocharged diesel engine by using artificial neural networks

Bolan Liu*, Changlu Zhao, Fujun Zhang, Tao Cui, Jianyun Su

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

59 引用 (Scopus)

摘要

This study presents a novel misfire detection model of a turbocharged diesel engine by using artificial neural network model. An explicit back propagation neural network has been developed to identify diesel combustion misfire according to the general engine operating parameters. The parameters are selected by using engine fault mode tree analysis. The proposed neural network model has been implemented in MATLAB/Neural Network Toolbox environment. Experimental study then has been performed on a V6 turbocharged diesel engine to get the parameters for both network training and validation purpose. Initial results show that misfire can be captured in most cases, however some mis-detection could happen though the mean square error of the model is satisfied. Furthermore, the in-cycle engine speed variation, a deductive parameter of transient engine speed, is added into the training data, which promotes the final results to full correct detection with no exception. The current study provides a new way to detect the happenings of misfire of turbocharged diesel engine.

源语言英语
页(从-至)26-32
页数7
期刊Applied Thermal Engineering
55
1-2
DOI
出版状态已出版 - 2013

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引用此

Liu, B., Zhao, C., Zhang, F., Cui, T., & Su, J. (2013). Misfire detection of a turbocharged diesel engine by using artificial neural networks. Applied Thermal Engineering, 55(1-2), 26-32. https://doi.org/10.1016/j.applthermaleng.2013.02.032