Study on Temperature Prediction of Mine Tape Conveyor Reducer Based On PSO-BP

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Yang, Yong^1 ; Cui, Chenchen^1 ; Guo, Xiucai^1 ; Wang, Qinsheng^1 ; Ren, Zhiqi^1

Xi'An University of Science and Technology, Xi'an

710054, China^1

关键词: BP neural networks;    Characteristic curve;    Daily production;    Fuzzy C mean clustering;    Particle swarm algorithm;    Prediction accuracy;    Pso-bp neural networks;    Temperature prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/5/052142/pdf
DOI  :  10.1088/1755-1315/252/5/052142

来源: IOP

【 摘 要 】

Mining tape Conveyor is an indispensable part of the daily production of coal mines. In order to avoid the fault of mine tape conveyor reducer as far as possible, based on the characteristics of mine tape conveyor system, this paper uses particle swarm algorithm to optimize BP neural network to predict the temperature of the transmission of the belt conveyor. Fuzzy c mean clustering denoising is carried out on the temperature data containing noise in the transmission of tape conveyor, and the temperature data containing noise are identified and the anomaly points are corrected by the characteristic curve. On the basis of temperature data preprocessing, the temperature prediction method of PSO-BP neural network for tape conveyor transmission is proposed. The simulation results show that the temperature prediction model of PSO-BP Neural network has the advantages of higher prediction accuracy and shorter convergence time, and has strong application significance.