Study of fault tolerance performance in fault diagnosis system based on NN model and data mining model

In practical application of intelligent fault diagnosis, mis-diagnosis may be caused by real-time information that is distorted in the process of their generation or transfer The probability of mis-diagnosis is dependent on the fault tolerance performance of intelligent principle used in diagnosis system. This paper researches the fault tolerance performance of fault diagnosis system based on neural network (NN) model and data mining (DV) model. It is discussed by diagnosis system of high voltage transmission line system (HVTLS). In DM model, the qualitative analysis ability of rough set (RS) theory is used to analyze knowledge region data set and the reducts of RS are solved by Genetic Algorithm (GA). In order to get the assurance of fault tolerance performance of tested diagnosis system and have practical value of studied system, this paper proposes the theory criterion of building test samples. The high fault tolerance performance of proposed approach is proved through comparison with that of NN-model based fault diagnosis system.