Fault Diagnosis Based on RSPNN for Pulverizing Systems

Due to the complicated relationship between the faults and corresponding symptoms of pulverizing systems,uncertainty of information,and the shortcomings of the general BP learning algorithm is training neural networks,a fault diagnosis system based on rough sets probabilistic neural networks(RSPNN)is proposed to deal with the traditional problems appearing in fault diagnosis techniques such as the sensitive initial value,the learning process into a local minimum and the slow training.Firstly,continuous attributes are quantized by SOM.Secondly,a HORAFA method based on distinguish matrix is used in the heuristic reduction of RS to reduce the samples as the input of the probabilistic neural networks(PNN).Then,the PNN is used as a classifier to predict fault.The simulation results show that the method optimizes the structure of neural network,decreases the computation complexity,improves the diagnosis correctness,and provides inspiration about on-line fault diagnosis for pulverizing system and related equipment.