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Nuclear power plant (NPP) is a complex system with abundant operation data and various fault types. Moreover, in most cases, change of system parameters and prompt of the alarm system can not necessarily tell us directly what types the fault belongs to or where it lies in. When multiple faults occur, there is no one-to-one relationship between the fault symptom and the fault itself. Furthermore, the degree and response rate of the fault are different, so some faults are gradual and some are sudden. It is thus clear that for different faults, we need to conduct combined diagnosis with a variety of diagnostic methods to ensure accuracy and instantaneity in NPP fault diagnosis. According to the characteristic of the distributed function of equipment and the digital instrument and control (I&C) system, we studied and designed the distributed condition monitoring and fault diagnosis system in NPP. Based on the “disassemble-synthesizing” diagnostic idea, this paper proposed an intelligent diagnosis method which applied the fuzzy neural network (FNN) in doing local diagnosis and multi-source information fusion technology in global diagnosis. The simulation result showed that this method can quickly and accurately complete the tasks of diagnosing different levels of the single fault and different types of multiple faults.

The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. 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 is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example. The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely.

The fuzzy logic and neural networks are combined in this paper, setting up the fuzzy neural network (FNN); meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN. The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system’s validity, the inverted U-tube break accident of steam generator is used as an example and many simulation experiments are performed. The test result shows that the FNN can identify the fault.

The work condition of automobile engine is terrible, so it has faults frequently. In order to guarantee the normal work of automobile, the intelligence fault diagnosis system has built. Fuzzy neural network technology is applied to the fault diagnosis system. Korean Hyundai GRANDEUR series DOHC engine is the diagnostic object. The test car has eight fault processing modules. Among them the ECU fault processing module is used to do experiments to set sensors faults and actuators faults. Several main faults of electronic controlled engine are diagnosed by the system on the test car. The analyses of the results indicate that the diagnosis system can diagnose the faults exactly in real time, and the precision is very high.

Data fusion method is applied in fault diagnosis field. The faults are diagnosed through three levels which are data fusion level, feature level and decision level respectively. The feature level uses multi-collateral neural networks. The purpose of using neural networks is mainly getting basic probability assignment (BPA) of D-S evidence theory. On the other hand the neural networks in feature level are used for local diagnosis and D-S evidence theory is adopted to integrate the local diagnosis results. This method is fit for complicated object. In order to improve the validity and practicability of this method using compare with single neural network to diagnose the same object faults. The results testify that data fusion method is superior to the single neural network method in diagnosing faults of complicated system.