Inference of power plant quake-proof information based on interactive data mining approach

This paper presents a nonlinear structural health inference technique, based on an interactive data mining approach. A mining control agent emulating cognitive process of human analysts is developed and integrated in the data mining loop, analyzing and verifying the output of the data miner and controlling the data mining process to improve the interaction between human users and computer system. 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. Based on proposed approach, an information inference system has been developed. To demonstrate how the proposed technique can be used as a powerful tool for inferring of structural health status in unclear power plant, quake-proof testing data have been applied.