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

When a fault such as unbalance occurs in a turbo-generator set, sensors should be put on its bearing to detect vibration signals for extracting fault symptoms, 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 the 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 the standard fault case database in order to improve accuracy of diagnosis system. Finally, a vibration fault diagnosis system for 600MW turbo-generator set is designed and realized by the proposed fuzzy neural network system structure, its ru...

• Type: Conference
• Year: 2006
• Where: ICAT
• Authors: Ping Yang, Qing-miao Wang
• Keywords: Fault Diagnosis, Fault Symptom, Fuzzy Neural Network, ICAT 2006