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Machine Learning Based Quality Control for Spent Fuel Safety Information
The spent fuel safety information delivered from the consignor to the disposal facility operator directly affects the operation and safety of the disposal facility. Therefore, the operator of a disposal facility must perform data quality management to increase data reliability, and anomaly detection is a representative method among quality control methods. We propose a quality control method to detect anomalies using XGBoost, known for its excellent performance, prevention of overfitting, and fast training speed. First, we select significant variables such as release burnup, enrichment, and amount U from the spent fuel safety information and train models for each variable using only normal data. A model trained using only normal data generates a small error for a normal pattern and a large error for an abnormal pattern. Then, when the data error exceeds a set threshold, the data is determined as an anomaly. In this paper, we implement the XGBoost models using virtual spent fuel information and optimize the hyperparameter of XGBoost using a simulated annealing method for high accuracy. The optimized XGBoost models show high accuracy in a normal input and provide a stable prediction value even in an abnormal input. In addition, we perform anomaly detection by including defect input in the data to validate the presented method. The proposed method shows the result of effectively classifying normal values and anomalies.
