Statistical Damage Detection in a Smart Structure under Different Temperatures via Vibration Testing: A Global Model Based Approach

Abstract

Statistical damage detection in a structure operating under different temperatures via vibration testing is addressed by means of a stochastic global model based approach. The approach relies upon novel global models of the Functionally Pooled (FP) form, which are capable of describing the dynamics under any temperature, and statistical decision making. In its present form the approach utilizes response (output–only) vibration data, although excitation–response data may be also used. Its effectiveness is confirmed via a large number of experiments performed on a smart composite beam under different temperatures within the [¡20; +20]oC range.