Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.

Keywords

Online damage detection, RLS filter, Smart structures, Structural health monitoring, T-test, Auto-regressive, Discrete-time, Feature identification, Hypothesis tests, Laboratory test, Nonstationary, Piezoelectric sensors, Recursive least squares, Square roots, Structural condition, Structural health, Test structure, Adaptive filtering, Adaptive filters, Aerodynamics, Algorithms, Damage detection, Electric filters, Linear systems, Online systems, Piezoelectricity, Structural dynamics, Testing