Study on intelligent fault diagnosis system based on compositive information fusion
Yusheng Zhang · Jisuanji gongcheng yu sheji · 2010
A structure of real-time intelligent fault diagnosis system based on compositive information fusion is designed.Method of clustering analysis and algorithm of genetic optimization is introduced to traditional BP neural-network,in order to reduce training difficulty of BP neural-network,and in order to increase training precision.Method of genetic NN and method of adaptive-NN-fuzzy-reasoning-system are integrated to information fusion diagnostic level in order to improve fault diagnosis reliability and to use diagnosis knowledge in training data fully.D-S evidence theory is used to analyze conclusions in the level of decision in order to utilize diagnosis result of each diagnosis cell effectively.It’s validated from emulational test that this fault diagnosis system can diagnose fault rapidly,exactly and reliably.