Detecting structural damage using adaptive feature extraction from transient signals

Abstract

The focus of this work is on damage detection in transient structural response time series data recorded during an underwater shock experiment. A unique data-driven approach where damage features are extracted, evaluated, and determined based on the instantaneous phases of structural waves was applied to detect damage for a large composite structure. Measured time series data was first decomposed adaptively into a set of basis functions, known as Intrinsic Mode Functions (IMFs), using the method of Empirical Mode Decomposition. Instantaneous phases are then defined based on the IMFs, which can be used to represent nonlinear and non-stationary signals. Damage features are then formulated and tracked in order to determine the state of a structure. This approach was developed based on a previously introduced fundamental relationship connecting the instantaneous phases of a measured time series to structural mass and stiffness parameters. A simple damage index based on the instantaneous phase relationship is used to show the effectiveness of this method for structural health monitoring applications.