Abstract
The present paper proposes a new diagnostic tool for the structural health monitoring that employs a statistical diagnosis of self-learning method using a system identification and statistical similarity test of the identified systems using F-test. Structural health monitoring is a noticeable technology for advanced composite structures and civil structures. Most of the structural health monitoring systems adopt parametric method based on modeling or non-parametric method such as artificial neural networks. The former method required modeling of each structures and latter method requires data for the training. And these modeling and data for the training demands much costs. The new statistic diagnosis method does not require the complicated modeling and a learning data of damaged structure for the artificial neural networks. The present study deals monitoring of delamination of composite beam using change of strain judged by the statistical tools such as Response Surface and F-Statistic. Response surfaces among the measured strain data of surface of composite beam are produced at intact state and monitoring state, and the difference of the each response surfaces are statistically tested using F-test. As a result, the new method successfully diagnoses the damage without using modeling and a learning data of damaged structure.
