PREDICTION OF CUTTING LOAD OF DRUM SHEARER BASED ON DEEP BELIEF NETWORK - MAO Jun; GUO Hao; CHEN HongYue (Jixie qiangdu)
A prediction model based on Deep Belief Network（ DBN） was proposed to accurately predict the cutting load of the shearer’s spiral drum. The DBN model uses 7 characteristic parameters which include 2 guiding boots,2 smooth boots,rocker arm vibration,idler shaft,and cutting motor current as visual input. By means of unsupervised level greedy learning,the higher level features are represented,the intelligence of the identification process is enhanced,and the complexity and imprecision of artificial features extraction are avoided. The test results show that the proposed method is suitable for predicting the load of the spiral drum of coal miner,which has strong characteristic extraction ability and better performance than BP neural network.