Gaussian mixture model and delay-and-sum based 4D imaging of damage in aircraft composite structures under time-varying conditions

Abstract

Abstract In the field of structural health monitoring (SHM) of aircraft composite structures, piezoelectric sensor network and guided wave (GW) based imaging method has proved to be a promising damage monitoring method and has been widely researched. However, the current work has barely considered that aircraft structures are usually subject to random and complex time-varying conditions, which may introduce uncertainties into the acquired GW signals and make it hard to realize reliable damage imaging and localization. Aiming at this issue, this paper proposes a Gaussian mixture model (GMM) and delay-and-sum based 4D imaging method to achieve reliable damage monitoring of aircraft composite structures under time-varying conditions. In this method, the GMM is adopted to suppress the time-varying influence and to construct time-invariant feature signal which is only affected by damage. During the monitoring process, by continuously updating GMM and constructing time-invariant feature signal, the delay-and-sum based 4D imaging can be performed to generate a serial of images with damage gradually emerging, from which the damage can be accurately located. The method is validated on a stiffened carbon fiber composite plate within a temperature range from −20 °C to 60 °C. Validation results indicate that reliable damage imaging and localization under temperature variation is achieved.