OPTIMIZING STRATEGY ON ROUGH SET NEURAL NETWORK FAULT DIAGNOSIS SYSTEM

The association deficiency of neural network affects its further application in pattern recognition. Rough set theory(RS) is excellent in disposal of small incomplete sample data. Based on this, the basic optimizing strategy of RS BP neural network fault diagnosis is presented in this paper. Using the strategy, an optimized neural network model is established. In this model, RS is employed as a preprocess course, and the results of RS preprocess decide the model structure. So the model can decrease the number of the network input nerve cells effectively, and ameliorate network inner structural. The bearing fault data and grinding condition data are analyzed for the model. The results show that the strategy has better study efficiency and diagnosis accuracy. It is estimated that the optimized strategy may be further applied in fault diagnosis.