Twin Model-based Fault Detection and Tolerance Approach for In-core Self-Powered Neutron Detectors

分类： 物理学 >> 核物理学 提交时间： 2023-07-24

Chen, Jing Lu, Yanzhen Jiang, Hao Lin, Weiqing Xu, Yong

摘要： The in-core self-powered neutron detector (SPND) acts as a key measuring device for the monitoring of parametersand evaluation of the operating conditionsof nuclear reactors. Prompt detection and tolerance of faulty SPNDsare indispensable forreliable reactor management. To completely extract the correlated state information of SPNDs, we constructeda twin model based on ageneralized regression neural network (GRNN)that represents the common relationshipsamong overall signals. Faulty SPNDswere determined because of the functional concordance of the twin model and real monitoring systems, which calculated the error probability distribution between the model outputs and real values. Faultdetection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures. Aweighted K-nearest neighbor model was employed to reasonably reconstruct the valuesof the faulty signals and guarantee data purity. The experimental evaluation of the proposed method showedpromising results,with excellentoutput consistency and high detection accuracy for bothsingle- andmultiple-point faulty SPNDs. Forunexpected excessive failures, the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.

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