Time: 2022-01-10 | Counts: |
doi:10.16186/j.cnki.1673-9787.2020070089
Received:2020/07/28
Revised:2020/09/28
Published:2022/01/10
Intelligent recognition of coal and rock properties in shearer cutting process based on improved RBF neural network
DUAN Mingyu 1,2, YUAN Ruifu 3, YANG Yi 4
1.School of Mechenical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430070 , Hubei, China;2.Henan Dayou Energy Co. ,Ltd. ,Sanmen%ia 472300 , Henan, China;3.School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000 , Henan, China;4.School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000 , Henan, China
Abstract: Recognition of the boundary between the coal and rock is the key issue of adjusting the drum high of the shearer. In order to recognize the boundary exactly without any other equipment,an intelligent method based on improved RBF neural network to identify the boundary according the different properties during the shearer cutting the coal and rocks. In this method,the different properties were reflect by the cutting current, traction current and the resistance of the hydraulic cylinder adjusting the height of the arm. Hence,the improved RBF neural network was used to analyze the properties,in which the parameters of the basis function inRBF neural network were optimized by the modified firefly algorithm. The verified experiments were carried out on the real data coming from 12150 workspace of Gengcun coal mine,and the experiment results showed that the recognition accuracy of the coal and rock property recognition model based on the improved RBF neural network proposed in this paper reached 93.94% . The method described in this paper could be used to identify coal and rock properties without additional detection equipment. It had high response speed and recognition rate,and had great engineering application potential.
Key words:recognition of coal and rock property;shearer;RBF neural network;firefly algorithm