Evaluation of the damage detection capability of a sparse-array guided-wave SHM system applied to a complex structure under varying thermal conditions

Abstract

A sparse-array structural health monitoring (SHM) system based on guided waves was applied to the door of a commercial shipping container. The door comprised a corrugated steel panel approximately 2.4 m by 2.4 m surrounded by a box beam frame and testing was performed in a nonlaboratory environment. A 3-D finite element (FE) model of the corrugations was used to predict transmission coefficients for the A0 and S0 modes across the corrugations as a function of incidence angle. The S0 mode transmission across the corrugations was substantially stronger, and this mode was used in the main test series. A sparse array with 9 transducers was attached to the structure, and signals from the undamaged structure were recorded at periodic intervals over a 3-week period, and the resulting signal database was used for temperature compensation of subsequent signals. Defects in the form of holes whose diameter was increased incrementally from 1 to 10 mm were introduced at 2 different points of the structure, and signals were taken for each condition. Direct analysis of subtracted signals allowed understanding of the defect detection capability of the system. Comparison of signals transmitted between different transducer pairs before and after damage was used to give an initial indication of defect detectability. Signals from all combinations of transducers were then used in imaging algorithms, and good localization of holes with a 5-mm diameter or above was possible within the sparse array, which covered half of the area of the structure.

Enhanced detection through low-order stochastic modeling for guided-wave structural health monitoring

“…[4][5][6][7][8][9][10][11] These studies in turn demonstrate the feasibility of their approach through some qualitative or sometimes quantitative assessment of the raw GWSHM images formed from features. The focus of these studies, however, leaves them without consideration of the stochastic nature of the extracted features.…”

Multi-mode and multi-frequency guided wave imaging via chirp excitations

“…Images were generated by applying the delay-and-sum method 13,14 to the envelope-detected, differential signals (i.e., residual signals after baseline subtraction) for different tone burst frequencies and numbers of cycles. Based upon the signal plots of Section 2, images are generated at 100 kHz with the A 0 guided wave mode, and at 400 kHz with the S 0 mode.…”

Uncertainty quantification for acoustic nonlinearity parameter in Lamb wave-based prediction of barely visible impact damage in composites

“…Propagating in either symmetric or anti-symmetric modes, Lamb waves are multimodal and dispersive in nature, usually with several modes propagating at the same time with respective velocities. Prevailing signal features of interest for the purpose of damage identification can be the delay in time-offlight (ToF) [17][18][19], wave reflection/transmission [20,21], mode conversion [3], or energy dissipation [4]. These signal features, as briefly mentioned earlier, are usually based on the assumption of linear wave propagation and linear material property changes (due to structural damage), and are therefore referred to as linear features in this study.…”