Application of SPEA2-MMBB for Distributed Fault Diagnosis in Nuclear Power System

Accurate fault diagnosis in nuclear power systems is essential for ensuring reactor stability, reducing the risk of potential faults, enhancing system reliability, and maintaining operational safety. Traditional diagnostic methods, especially those based on single-system approaches, struggle to address the complexities of composite faults and highly coupled fault data. In this paper, we introduce a distributed fault diagnosis method for nuclear power systems that leverages the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for multi-objective optimization and a modified MobileNetV3 neural network with a Bottleneck Attention Module (MMBB). The SPEA2 algorithm is used to optimize sensor feature selection, and the sensor data are then input into the MMBB model for training. The MMBB model outputs accuracy rates for each subsystem and the overall system, which are subsequently used as optimization targets to guide SPEA2 in refining the sensor selection process for distributed diagnosis. The experimental results demonstrate that this method significantly enhances subsystem accuracy, with an average accuracy of 98.73%, and achieves a comprehensive system accuracy of 95.22%, indicating its superior performance compared to traditional optimization and neural network-based approaches.