Nuclear Power Plants Fault Diagnosis Method Based on Data Fusion

The data fusion is a method suit for complex system fault diagnosis such as nuclear power plants, which is multisource information processing technology. This paper uses data fusion information hierarchical thinking and divides nuclear power plants fault diagnosis into three levels. Data level adopts data mining method to handle data and reduction attributes. Feature level uses three parallel neural networks to deal with attributes of data level reduction and the outputs of three networks are as the basic probability assignment of Dempster-Shafer (D-S) evidence theory. The improved D-S evidence theory synthesizes the outputs of neural networks in decision level, which conquer the traditional D-S evidence theory limitation which can’t dispose conflict information. The diagnosis method was tested using correlation data of literature. The test results indicate that the data fusion diagnosis system can diagnose nuclear power plants faults accurately and the method has application value.