Data-driven Prognosis of Fatigue-induced Delamination in Composites using Optical and Acoustic NDE methods

Abstract

With increasing use of fiber reinforced polymer (FRP) composites in several industries such as aviation, automotive and construction, effective reliability analysis of composites has become imperative in recent years. Periodic inspection by robust non-destructive evaluation (NDE) techniques and accurate health prognosis is essential for condition-based maintenance (CBM) of the safety-critical components and structures. Prediction of future damage level in composites often becomes challenging due to lack of physics-based damage growth models for unknown materials which leaves us to rely solely on the NDE data for prognosis. In this study, delamination growth in glass fiber reinforced polymer (GFRP) joints, under Mode I cyclic loading, was monitored by guided waves(GW) using miniature surface-mounted piezoelectric wafers(PZT). Data-driven prognosis techniques such as Kalman filter and particle filter were implemented on the indirect CBM data obtained from GW signals to predict future delamination area and validated against optical transmission scans (OTS) of damaged samples. A comparison of data-driven prognosis methods with static regression versus dynamic update of estimated parameters is presented in this paper. Results show that even when a simple logarithmic fit is assumed, use of NDE data to estimate function parameters in a stochastic framework outperforms the static regression approach leading to a robust sensor-aided reliability analysis.