关键词: 采煤机, 载荷预测, 深度信念网络, 贝叶斯正则化, 改进粒子群优化

Abstract: To solve the problems of low accuracy using single sensor for cutting loads of shearers and improve the sensing ability of shearer running safety states, based on the applications of DBN, BR and PSO algorithm, a multi-sensor shearer drum load identification and prediction method was proposed. BR-PSO-DBN prediction model was constructed using 22 sets of test data monitored by idler shaft sensors, connecting frame pin shaft sensors and rocker strain sensors as input samples to predict the three directional loads and torque of drum cutting. Based on the experimental system of cutting loads, the field test was carried out, and the measured results were compared with the predicted ones. The results show that the measured curves are basically the same as the predicted curves, and the peak points correspond to each other. The prediction accuracy of the model for the three-way cutting loads of the drum is more than 83%, and the prediction accuracy of the roller torque is 95%, which indicates that the prediction model has high prediction accuracy and may provide reference for field applications to guide safety productions.

Key words: shearer, load prediction, deep belief network(DBN), Bayesian regularization(BR), improved particle swarm optimization(PSO)