A Neural Network and Post-processing for Estimating the Values of Error Data

A sensor network is a key factor for successful structural health monitoring (SHM). Although stable sensor network system is deployed in the structure for measurement, it is often inevitable to face measurement faults. In order to secure the continuous evaluation of targeted structure in cases where the measurement faults occur, appropriate techniques to estimate omitted or error data are necessary. In this research, backpropagation neural network is adopted as a basic estimation method. Then, a concept of post-processing is proposed to improve an accuracy of estimation obtained from the neural network. The results of simulation to verify performance of estimation are also shown.