A fault diagnosis method of coal mine belt conveyor - YANG Qingxiang; XIANG Xiuhua; MENG Bin; WANG Kaizhong (Gong-kuang zidonghua, 2017)
In view of problems of timeliness and reliability of fault diagnosis for coal mine belt conveyor are seriously affected by various fault types and the mutual influence of symptoms, a fault diagnosis method of coal mine belt conveyor was put forward. The method adopts fault diagnosis technologies combining with rough set and neural network, uses rough set attribute reduction algorithm to optimize input fault symptoms set, and obtains the optimal reduction set. The reduced minimum condition attribute set was input into BP neural network to train in a reasonable manner, and diagnosis decision rules was obtained through continuous learning and optimization. The reduced samples of the corresponding test symptoms set attribute were input into the trained network to diagnose fault, so as to identify corresponding fault. The simulation results show that the method can fully remove redundant information, speed up network training, and improve fault diagnosis accuracy of belt conveyor.