Reliable Damage Monitoring Under Time-Varying Conditions Based on Gaussian Mixture Model and Delay-and-sum Imaging

Abstract

As an effective structural health monitoring (SHM) technology, piezoelectric sensor network and guided wave (GW) based damage imaging method has been widely researched. However, aircraft structures are usually subject to random and nonlinear time-varying conditions, making it hard to achieve reliable damage imaging and localization result. Aiming at this issue, this paper proposes a Gaussian mixture model (GMM) and delay-and-sum imaging based reliable damage monitoring method. In this method, the GMM is used to suppress the uncertainties on GW signals caused by timevarying conditions and to construct the time-invariant feature signal which is able to reliably describe the damage induced signal change. After obtaining the time-invariant feature signals of every GW pitch-catch path in the adopted sensor network, the delayand- sum imaging can be performed to realize reliable and accurate damage imaging and localization. In order to verify the feasibility of the proposed method, experiments are conducted on a stiffened carbon fiber composite plate within a temperature range from -20℃ to 60℃. The experimental results show that the method is able to suppress the time-varying influence and locate damage accurately under temperature variation.