A fault diagnosis modeling method combined RBF neural network with rough set theory

In order to improve diagnosis precision and decreasing misinformation diagnosis, according to the intelligence complementary strategy, a new complex intelligent fault diagnosis method based on rough sets theory and RBF neural network is presented. Firstly, basis on data pretreatment, the fault diagnosis decision table is formed, and continuous datum are discretized by using hybrid clustering method. Rough sets theory as a new mat hematical tool is used to deal with inexact and uncertain knowledge for pattern recognition. The target is mainly to remove redundant information and seek for reduced decision tables which to obtain he minimum fault feature subset. The neural networks adopted were of the feed-forward variety with one hidden layer. They were trained using back-propagation. The method can reduce the false alarm rate and missing alarm rate of the fault diagnosis system effectively, and can detect the composed faults while keep good robustness.