Research on Data Fusion Diagnosis System Based on Neural Network and D-S Evidence Theory

The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory integrates the local diagnosis results in decision level. The system diagnosed several main faults of gas turbine rotor on the tester. The results indicate that the diagnosis system can diagnose the faults exactly in real time, and the precision is very high.