Fault Diagnosis of Metro Shield Machine Based on Rough Set and Neural Network

Due to massive date to be monitored for Metro shield machine, in order to solve the problems of knowledge acquisition bottlenecks and complexity structure of network structure and long traing time which based on expert system and neural network fault diagnosis methods. This article will introduces rough set theory to the subway shield machine fault diagnosis, Propose a method which based on rough set theory combine with neural network of Metro shield machine fault diagnosis. Use the strong advantage of rough sets theory in data classification, Remove the data redundancy of information which not effective for decision-making. Then uses the reduced data as a sample. Application of neural network algorithm to reduce date for diagnosis, which can effectively improve the speed and accuracy of the diagnosis, thus preferable provide basis for fault diagnosis and decision-making.