Detection of Subtle Damage in Structures through Smart Signal Reconstruction

What is it about?

The primary function of structural health monitoring (SHM) is the process of extracting the damage features from the measured raw data, recorded using sensors on the structure of interest. The efficiency of SHM techniques lies in their capability to detect early damage, which alters the dynamic characteristics of only a few modal responses but in a feeble manner, in its incipient stage. Isolating these modal responses, hidden in the overall raw response, for damage diagnosis, is a real challenge to the SHM community. In order to handle this issue, an improved version of Empirical Mode Decomposition (EMD) is employed in this paper. EMD decomposes the measured response signals into mono-component signals, called intrinsic mode functions (IMFs). The mixed modes in EMD are handled using Intermittency criteria in the proposed EMD. Once the IMFs are extracted from the raw signal, the IMFs (signal components) which possess the valuable information of incipient damage called ‘critical IMFs’, are isolated. To determine the spatial location of damage, these critical IMFs are combined to reconstruct a new signal with enriched information on minor/incipient damage. ARMAX model is employed on the new signal with enriched damage information. A normalized distance measure of ARMAX models, in terms of subspace angles, is used as a damage indicator. The numerical and experimental investigations presented in this paper clearly reflect that the proposed output-only damage diagnostic technique using the smart reconstruction of the measured raw signal is capable of detecting the incipient subtle damages in the structures.

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