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.
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Acknowledgment
This work is partially supported by the National Student’s Platform for Innovation and Entrepreneurship Training Program (201610143022), the Research Project of Education Department of Liaoning Province (L201630) and the Doctoral Start-up Research Foundation of Shenyang Aerospace University (15YB03).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Cheng, X., Zhao, L., Lin, N., Gong, C., Wang, R. (2018). A Machine Learning Based Engine Error Detection Method. In: Long, K., Leung, V., Zhang, H., Feng, Z., Li, Y., Zhang, Z. (eds) 5G for Future Wireless Networks. 5GWN 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-72823-0_32
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DOI: https://doi.org/10.1007/978-3-319-72823-0_32
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