doi:10.16186/j.cnki.1673-9787.2020.1.9

Received:2019/04/09

Revised:2019/05/20

Published:2020/01/15

Reliability prediction of shearer based on BP neural network optimized byparticle swarm

TIAN Zhen1,2, JING Shuangxi2, ZHAO Lijuan3, GAO Shan1, ZHANG Chengguang1

1.College of Mechanical and Electrical Engineering， Zhaukau Normal University， Zhaukau  466000 ， Henan， China;2.School of Mechanical and Power Engineering， Henan Polytechnic University， Jiaozuo  454000 ， Henan， China;3.College ofMechanical Engineering， Liaoning Technical University， Fuxin  123000 ， Liaoning， China

Abstract:In order to study the reliability of shearer cutting process， the reliability prediction model of shearer was established based on the BP neural network optimized by particle swarm. The membership function between the stress and structure reliability of the material was constructed by using the Gauss shaped membership function and the reliability of the shearer was determined by means of orthogonal simulation test. The test results were used as learning samples to test the accuracy of the prediction model. The maximum relative error between prediction results and experimental results was 2. 61 % ， which met the accuracy requirements. The reliability of the shearer with different hardness and cutting depth under different traction speed was analyzed by using prediction model， and the rules of reliability of shearer with the three changes was found. With the coal seam hardness and traction speed increased， the greater the degree of reliability reduction would get， and with the cut depth increased， gradually the degree of reliability reduction gradually got flat.

Key words:shearer;reliability prediction;particle swarm optimization algorithm;BP neural network

基于粒子群优化BP神经网络的采煤机可靠性预测_田震.pdf