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
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.