State Parameter Generation Model of Comprehensive Coal Mining Machinery Based on Petri Net and Deep Learning

Authors

  • Haitao He, Wei Guo, Yuan Wang, Chao Wang, Shuan Feng Zhao

Abstract

The traditional petri net modeling model cannot obtain the operating state parameter values of the comprehensive coal mining machinery. However, these state parameter values are the input values required by the coal flow adaptive control, which is of great significance to the coal flow adaptive control. Therefore, this paper proposes a state data generation model for comprehensive coal mining machinery based on Petri nets and deep learning. Firstly, establish a comprehensive coal mining process hybrid modeling module (HPN) to analyze the comprehensive coal mining process. Secondly, build the operating state generation module (DMGAN) of the comprehensive coal mining machinery to achieve the mapping between the operating parameters of the comprehensive coal mining machinery and the actual comprehensive coal mining process. Next, obtain the operating state parameter value of the comprehensive coal mining mechanical device at any time through HPN+DMGAN. Finally, simulate the model in this paper with Yujialiang 43101 fully mechanized mining work. The experimental results show that the model in this paper can obtain the specific operating status data of the shearer and scraper conveyor in a certain state through simulation experiments, which is of great significance to the adaptive control of coal flow.

Published

2020-04-30

Issue

Section

Articles