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Different types of faults could occur in a nuclear power plant, and there was no direct correspondence between a specific fault and its symptoms. So, a hybrid intelligence approach is proposed for the fault diagnosis at a nuclear power plant. Depending up the symptoms observed and the progress of fault diagnosis process, different fault diagnosis technologies, such as artificial neural network, data fusion and signed directed graph, could be combined as appropriate to detect and identify different faults at local or global level in nuclear power plants. The effectiveness of hybrid intelligence approach in improving the fault diagnosis efficiency in nuclear power plants was verified through simulation experiments. [ABSTRACT FROM AUTHOR]

In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system. [ABSTRACT FROM AUTHOR]

A dynamic model was developed using two back-propagation neural networks of the same structure, one for online training and the other for prediction, and proposed for continuous dynamic prediction of the time series of NPP operating parameters. The proposed prediction model was validated by predicting such time series of NPP operating parameters as coolant void fraction, water level in SG and pressurizer. Validation results indicated the proposed model could be used to achieve a stable prediction effect with high prediction accuracy for the prediction of fluctuating data. [ABSTRACT FROM AUTHOR]

A minimum dose method based on staff walking road network model was proposed for the walking-path planning in nuclear facilities. A virtual–reality simulation program was developed using C# programming language and Direct X engine. The simulation program was used in simulations dealing with virtual nuclear facilities. Simulation results indicated that the walking-path planning method was effective in providing safety for people walking in nuclear facilities. [ABSTRACT FROM AUTHOR]

This paper proposes a sampling-based method (SBM) for planning minimum dose path navigation for workers in nuclear facilities. To simulate and avoid obstacles, they were approximated by a combination of basic geometries and detected with the aid of virtual reality. To obtain an optimized walking path with minimum dose without considering the pre-designed road networks, a sampling-based method that utilizes the rapidly exploring random tree star (RRT*) algorithm, applicable in both continuous and discrete radiation fields, was proposed and implemented. The performance of this method was evaluated by simulating walk paths and estimating their radiation dose rates in a hypothetical nuclear facility, and the simulation results were compared with those derived from Dijsktra's algorithm under various radioactive environments. [ABSTRACT FROM AUTHOR]