TY - GEN
T1 - A state and fault prediction method based on RBF neural networks
AU - Tao, Yong
AU - Zheng, Jiaqi
AU - Wang, Tianmiao
AU - Hu, Yaoguang
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/4
Y1 - 2016/11/4
N2 - A state and fault prediction method based on RBF neural networks is proposed. The agricultural machinery is chosen as the experimental object of the method. There are 4 health level, such as failure, hazardous, sub-healthy and healthy. Some data of different provinces have been obtained, the health level can be acquired by RBF neural networks. The mathematical model of agricultural machinery is difficult to be proposed in this paper, so the traditional control algorithm can't be used in agricultural machinery. However, the RBF neural networks can solve this problem. At the same time, some vital factors should be considered, such as mileages, rotational speed, stubble height, water temperature, oil pressure of agricultural machinery. The rotational speed and stubble height have a big effect on fault prediction of agriculture. The experimental results verify the effectiveness of the proposed method.
AB - A state and fault prediction method based on RBF neural networks is proposed. The agricultural machinery is chosen as the experimental object of the method. There are 4 health level, such as failure, hazardous, sub-healthy and healthy. Some data of different provinces have been obtained, the health level can be acquired by RBF neural networks. The mathematical model of agricultural machinery is difficult to be proposed in this paper, so the traditional control algorithm can't be used in agricultural machinery. However, the RBF neural networks can solve this problem. At the same time, some vital factors should be considered, such as mileages, rotational speed, stubble height, water temperature, oil pressure of agricultural machinery. The rotational speed and stubble height have a big effect on fault prediction of agriculture. The experimental results verify the effectiveness of the proposed method.
UR - https://www.scopus.com/pages/publications/85006999641
U2 - 10.1109/ARSO.2016.7736285
DO - 10.1109/ARSO.2016.7736285
M3 - 会议稿件
AN - SCOPUS:85006999641
T3 - Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO
SP - 221
EP - 225
BT - 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016
PB - IEEE Computer Society
T2 - 2016 IEEE Workshop on Advanced Robotics and its Social Impacts, IEEE ARSO 2016
Y2 - 7 July 2016 through 10 July 2016
ER -