The internal threat to nuclear security is one of the most serious problems in the physical protection supervision of spent fuel reprocessing plants. Both insiders and nuclear security departments have obvious characteristics of situational decision making and even irrational decision making. Combined with Game theory and RDEU theory, the RDEU Game model of insiders and nuclear security departments was constructed to analyze the existence of equilibrium solutions of two-way strategies under different emotional states. From a dynamic point of view, the influence and change process of emotion on participants’ decision-making behavior were analyzed. Then, the model was numerically simulated to verify its accuracy and effectiveness, which showed that different emotional states and intensities would not only affect the final result of evolutionary equilibrium, but also change the evolution speed of the strategies. In addition, compared with insiders, the intensity of pessimism in the nuclear security department had a greater impact on the game equilibrium. Finally, we present some reasonable recommendations to prevent and protect nuclear security events at spent fuel reprocessing plants by strengthening the emotional supervision and guidance of insiders and the nuclear security department.

Once an emergency event (EE) happens, emergency decision-making (EDM) plays a key role in mitigating the loss. EDM is a complex problem. Compared with conventional decision-making problems, more experts participate in decision-making. It usually has the feature of large group emergency decision-making (LGEDM). This paper proposes a large group emergency decision-making method based on Bayesian theory, relative entropy, and Euclidean distance, which is used for large group emergency decision-making with uncertain probabilities of occurrence, unknown attribute weights, and expert weights. In order to improve the accuracy of decision-making, Bayesian method is introduced into the calculation of scenario probability in the process of LGEDM. In the decision-making process, the experts’ risk preference is considered. The experts’ decision preference information is a symmetric and uniformly distributed interval value. The perceived utility values of the experts are obtained by introducing prospect theory. Euclidean distance is used to measure the contributions of experts to aggregation similarity, and different weights are given to experts according to their contributions. A relative entropy model with completely unknown weight information constraints is established to obtain attribute weights, which takes into account the differences of different alternatives under the same attribute and the differences between alternatives and the ideal solution. An example of nuclear power emergency decision-making illustrates the effectiveness of this method.

Spent fuel shearing machines in nuclear power plants are important equipment for the head end of spent fuel reprocessing in power reactors. Condition monitoring and fault diagnosis play important roles in ensuring the safe operation of spent fuel shearing machines, avoiding serious accidents, and reducing their maintenance time and cost. Existing research on fault diagnosis of spent fuel shearing machines has some shortcomings: (a) the current research on fault diagnosis of shearing machines is small and diagnostic accuracy is not high. The research methodology of shearing machines needs to be updated; (b) the high difficulty in obtaining fault data and the often limited and highly informative fault data for shearing machines lead to low diagnostic performance. To solve these problems, this study constructs a residual network (ResNet) model based on Bayesian optimization (BO) and convolutional block attention module (CBAM). First, dual-channel difference method is introduced into the preprocessing of noise signals, and two data enhancements were applied to the Mel spectrograms used as inputs to the model. Second, the attention mechanism CBAM is introduced to improve the ResNet to enhance the deep feature extraction ability of the network, and the BO algorithm is used to train the hyperparameters, such as the optimizer, and retrain the network model after obtaining the optimal hyperparameters. Finally, the feasibility and effectiveness of the proposed model are verified through experiments on the noise signals of spent fuel shearing machines. The experimental results show that the diagnostic accuracy of the constructed model is 93.67%, which is a significant improvement over the other methods.

Shearing machines are the key pieces of equipment for spent–fuel reprocessing in commercial reactors. Once a failure happens and is not detected in time, serious consequences will arise. It is very important to monitor the shearing machine and to diagnose the faults immediately for spent–fuel reprocessing. In this study, an intelligent condition monitoring approach for spent nuclear fuel shearing machines based on noise signals was proposed. The approach consists of a feature extraction based on wavelet packet transform (WPT) and a hybrid fault diagnosis model, the latter combines the advantage on dynamic–modeling of hidden Markov model (HMM) and pattern recognition of artificial neural network (ANN). The verification results showed that the approach is more effective and accurate than that of the isolated HMM or ANN.

As a decision information preference which includes membership degree (MD), non-membership degree (NMD), and probability, the probabilistic dual hesitant fuzzy set (PDHFS) is a crucial tool for effectively expressing uncertain information. In the domains of multi-attribute decision making (MADM) and multi-attribute group decision making (MAGDM), distance measures are extremely helpful tools. In this study, a novel PDHFS distance measure is put out, on which a MAGDM method that takes decision-makers’ (DMs’) psychological behavior into account is proposed. First, a novel distance measure is put forward to effectively assess the difference between different PDHFSs by adding consideration of the distances between MDs and between NMDs. Second, a similarity-trust analysis method based on the new distance measure is employed to calculate expert weights for integrating group decisions, and the group satisfaction index and regret theory are extended to a probabilistic dual hesitant fuzzy information environment. A MAGDM method based on the novel distance measure and regret theory is proposed. Finally, the proposed method is applied to the selection of radiation protection strategies in nuclear power plants, and it is also determined through parametric analysis that DMs’ tendency to avoid regret has an impact on the outcomes of decisions. When the method proposed in this study is compared to existing approaches, the findings demonstrate that the method’s performance in resolving MAGDM issues in a PDHFS environment is superior.