Sensors and signal processing for guided waves SHM

Abstract

The use of ultrasonic guided waves (GWs) for structural health monitoring (SHM) has interested many researchers. Nevertheless, Lamb-wave testing for SHM is complicated by the dispersive nature of wave modes, which deteriorates the wave spatial resolution and makes the experimental data hard to interpret. To tackle this problem, methods which analyze the dispersive signals in the domain spanned by time-frequency representations (TFRs) have been proposed. An infinite number of TFRs and processing tools can be obtained by using unitary transformations. Unitary operators such as the Warped Frequency Transform (WFT) are particularly suited for the analysis of GWs. This seminar will focus on WFT-based analysis methods capable to achieve sparse representations of GW signals. These methods naturally lead to super-resolvedand artifact-free representations, even in noisy environments, and are particularly effective to extract the information on the wave distance of propagation. The concept of sparse representations is also the basis of the so-called compressive sensing (CS) theory, which offers an intriguing alternative with respect to the classical process of acquiring signals according to the Shannon–Nyquist paradigm. CS theory proves that a signal which is sparse in a given representation can be compressed directly at the sampling stage. In this talk, a CS framework for Lamb wave field acquisitions with air-coupled probes or laser-Doppler vibrometers will be presented. The proposed framework is intended to minimize the number of ultrasonic scan point locations over the surface of the inspected structure. The implemented procedure is based on the selection of suitable sparsity-promoting TFR domains and on dedicated sample-point distribution strategies, achieving highly effective recovery results while starting from highly incomplete wave-field data.