Detecting Structural Damage of Nuclear Power Plant by Interactive Data Mining Approach

This paper presents a nonlinear structural damage identification technique, based on an interactive data mining approach, which integrates a human cognitive model in a data mining loop. A mining control agent emulating human analysts is developed, which directly interacts with the data miner, analyzing and verifying the output of the data miner and controlling the data mining process. Additionally, an artificial neural network method, which is adopted as a core component of the proposed interactive data mining method, is evolved by adding a novelty detecting and retraining function for handling complicated nuclear power plant quake-proof data. Plant quake-proof testing data has been applied to the system to show the validation of the proposed method.