Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

The in-core self-powered neutron detector (SPND) acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors. Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management. To completely extract the correlated state information of SPNDs, we constructed a twin model based on a generalized regression neural network (GRNN) that represents the common relationships among overall signals. Faulty SPNDs were 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. Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures. A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity. The experimental evaluation of the proposed method showed promising results, with excellent output consistency and high detection accuracy for both single- and multiple-point faulty SPNDs. For unexpected excessive failures, the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.