A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

The hydraulic heightening system is the core component of shearer, and its stable operation directly affects safety reliability equipment, so it great significance to realize an efficient accurate fault diagnosis. This paper proposes a diagnosis method combining rough set radial basis function neural network (RS-RBFNN). Firstly, RS used discretize original data attribute reduction, remove redundant information, mine implicit knowledge potential rules. Then, topology structure RBFNN determined. mapping relationship established between symptom category. carried out with Python language. Finally, compared two diagnostic methods including back propagation (BPNN) RBFNN. research results show that RS-RBFNN has highest accuracy, average 98.68%, which verifies effectiveness proposed method.