Fault Diagnosis System for Turbo-Generator Set Based on Fuzzy Neural Network

When a fault such as unbalance occurs in a turbogenerator set, sensors should be put on its bearing to detect vibration signals for extracting fault symptoms and then diagnose faults. But the relationships between faults and fault symptoms are too complex to get enough accuracy for industry application. In this paper, a new diagnosis method based on fuzzy neural network is proposed and a fuzzy neural network system is structured by associating fuzzy set theory with neural network technology. Especially, an effective fuzzy organization method for training samples is presented, fault symptoms are discretized by a focusing quantization method and are then fuzzified to obtain fuzzy sets. In addition, the standard fault data which is confirmed by application is added to standard fault case database in order to improve accuracy of diagnosis system. Finally, a vibration fault diagnosis system for 600 MW turbo-generator set is designed and realized by the proposed system structure based on fuzzy neural network, its running results showed that the new system could satisfy fault diagnosis requirement of large turbo-generator set, its accuracy varied from 92 percent to 98 percent.