Card fault diagnosis of the pressurized water reactor off-heap nuclear measurement system based on expert experience and convolutional neural network

The reactor nuclear measurement system is important in a nuclear power plant. Its main role is to measure the reactor's core power distribution using detectors and calibrate and provide data on the core fuel consumption. This study describes the lack of fault data and the lack of diagnostic methodology research in the overhauling process and fault diagnosis of the off-heap nuclear measurement system core card. This core card provides the detectors with the necessary working conditions. It also collects signals. In this study, we propose a methodology for the fault diagnosis of the card through circuit analysis, simulation of functional module division, fault data generation, and training of a convolutional neural network diagnostic model. The proposed methodology can transform the drawings into convenient diagnostic processes and algorithms based on expert experience. These drawings are difficult to use in actual overhauling conditions. The corresponding experimental equipment was designed for practical testing. The experimental results show that the accuracy of the obtained diagnostic model for classifying preset faults can reach 99.5%, indicating that this model can be applied in actual working conditions. The accuracy of the trained diagnostic model in classifying 13 kinds of faults in the training set during the actual test was tested. Results show that the accuracy rate is close to 100%. Moreover, the correction of the model using the real maintenance data in applying the actual maintenance conditions was also analyzed. The intelligent diagnostic system that centers on the fault diagnosis method investigated in this study has been applied in the pressurized water reactor off-heap nuclear measurement system digital transformation and upgrading project of Qinshan No. 2 Plant.