In view of the lower accuracy of the vibration fault diagnosing system for turbo-generator units

a self-organized fuzzy neural network system is built by combining the fuzzy set theory with the neural network technology. In particular

an effective fuzzy self-organized method for training samples of the neural network is presented. The fault symptoms are firstly extracted from fault signals via the FFT algorithm and graphics analysis

discretized by a focusing quantization algorithm and then fuzzified by means of the given fuzzy sets. The standard sample database for the diagnosing neural network is established. Because the focus center of the focusing quantization algorithm is placed in the transition area from a normal state to an abnormal one of fault symptoms

the resolution near the focus should be enhanced so that the diagnostic accuracy of the fuzzy neural network diagnosing system can be appreciably improved. In addition

the fault data confirmed by actual application is added to the standard fault case database in order to improve the accuracy of the diagnosing system as a whole. Finally

supported by a special detecting system

a vibration fault diagnosis system for a 600 MW turbo-generator unit is designed and implemented. More importantly

its running results in a thermal power plant of Guangdong Province show that the new diagnosing system can meet the requirements of fault diagnosis of large turbo-generator units with the accuracy varying from 96% to 100%.