Applications of Rough Set Theory in Intelligent Fault Diagnosis
Lina Hao · Zhongguo jixie gongcheng · 2002
Neural network and rule reasoning are two important methods of intelligent fault diagnosis. In this paper we give two applications of Rough Set in the fault diagnosis:(1) Rough Set-Neural Network(RNN)system, i.e. Rough Set acts as preprocessing of the Neural Network system,the simulation results indicate that the RNN system has increased correct rate and velocity of diagnosis. (2) Rough Set is used to rule acquisition of fault diagnosis expert system, and can elicit certain rules and possibility rules. The results of this paper indicate that the Rough Set method can solve the problem of rule acquisition under the condition of imprecise and inconsistent information on account of kind overlapping, and can eliminate the effects of misinformation and failing to report on the quality of diagnosis in fault diagnosis.