A dynamic load identification method of roadheader

Abstract

For problems of varied roadheader loads and difficult real-time identification of dynamic load during roadheader working, a dynamic load identification method of roadheader was proposed which was based on multi neural networks and evidence theory. In the method, vertical, horizontal and axial components of vibration signal are analyzed separately by use of RBF neural network, then preliminary results from RBF neural network are fused by use of D-S evidence fusion theory, so as to identify dynamic load of roadheader real-timely. The analysis of actual example show that accuracy rate of dynamic load identification of roadheader achieves 88%.

Keywords

• roadheader
• dynamic loading identification
• adjustment of cutting speed
• multi neural networks
• evidence theory