5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings

Research Article

A Machine Learning Based Engine Error Detection Method

  • @INPROCEEDINGS{10.1007/978-3-319-72823-0_32,
        author={Xinsong Cheng and Liang Zhao and Na Lin and Changqing Gong and Ruiqing Wang},
        title={A Machine Learning Based Engine Error Detection Method},
        proceedings={5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings},
        proceedings_a={5GWN},
        year={2018},
        month={1},
        keywords={Self-organizing neural network Elman neural network Probabilistic neural network Engine fault},
        doi={10.1007/978-3-319-72823-0_32}
    }
    
  • Xinsong Cheng
    Liang Zhao
    Na Lin
    Changqing Gong
    Ruiqing Wang
    Year: 2018
    A Machine Learning Based Engine Error Detection Method
    5GWN
    Springer
    DOI: 10.1007/978-3-319-72823-0_32
Xinsong Cheng1,*, Liang Zhao1,*, Na Lin1,*, Changqing Gong1,*, Ruiqing Wang2,*
  • 1: Shenyang Aerospace University
  • 2: Beijing University of Posts and Telecommunications
*Contact email: 18842458912@163.com, lzhao@sau.edu.cn, linna@sau.edu.cn, gongchangqing@sau.edu.cn, wrq@bupt.edu.cn

Abstract

Nowadays the fault of automobile engines climb due to the growth of automobiles. Traditional mechanical automobile testing is not efficient enough. In this paper, the Machine Learning based Engine Error Detection method (MLBED) is proposed for the complex nonlinear relation and operation parameters of automobile engine operating parameters such as large scale data, noise, fuzzy nonlinear etc. This method is a fault diagnosis and early warning method designed on the basis of self-organizing neural network, Elman neural network and probabilistic neural network. The experimental results show that MLBED has a great advantage in the current fault detection methods of automobile engine. The method improves the prediction accuracy and efficiency.