Rough Sets -Least square and Neural Networks in Fault Diagnosis Shield Applied Research

Shield is a typical mechanical, electrical, hydraulic integration of equipment. Its faults species are complex and diverse. To prevent because machine failure causes economic losses and casualties by shield, this article will introduces rough set theory to the subway shield machine fault diagnosis, propose a method which is based on rough set theory combined with neural network of Metro shield machine fault diagnosis. Use the strong advantage of rough sets theory in attribute reduction, and remove the data redundancy of information which is not effective for decisionmaking. Application of neural network algorithm to reduce date for diagnosis, the method can effectively improve the speed and accuracy of the diagnosis. Then use BP neural network combined with least square method to forecast fault, the least-square can reflect the trend of linear sequence, Neural network can seize the variation of nonlinear time series, therefore the combination of two methods could well predict the future of unit operating conditions.