ABSTRACT
In the context of an embedded structural health monitoring (SHM) system, two methods of transducer integration into the core of a laminate carbon fiber-reinforced polymer (CFRP) are tested: cut-out and between two plies. This study focuses on the effect of integration methods on Lamb wave generation. For this purpose, plates with an embedded lead zirconate titanate (PZT) transducer are cured in an autoclave. The embedded PZT insulation, integrity, and ability to generate Lamb waves are checked with electromechanical impedance, X-rays, and laser Doppler vibrometry (LDV) measurements. Lamb wave dispersion curves are computed by LDV using two-dimensional fast Fourier transform (Bi-FFT) to study the quasi-antisymmetric mode (qA0) excitability in generation with the embedded PZT in the frequency range of 30 to 200 kHz. The embedded PZT is able to generate Lamb waves, which validate the integration procedure. The first minimum frequency of the embedded PZT shifts to lower frequencies and its amplitude is reduced compared to a surface-mounted PZT.
ABSTRACT
This paper presents the use of a kernel-based machine learning strategy targeting classification and regression tasks in view of automatic flaw(s) detection, localization and characterization. The studied use-case is a structural health monitoring configuration with an array of piezoelectric sensors integrated on aluminium panels affected by flaws of various positions and dimensions. The measured guided wave signals are post processed with a guided wave imaging algorithm in order to obtain an image representing the health of each specimen. These images are then used as inputs to build classification and regression models. In this paper, an extensive numerical validation campaign is conducted to validate the process. Then the inversion is applied to an experimental campaign, which demonstrate the ability to use a numerically-built model to invert experimental data.
ABSTRACT
In this paper, imaging results of defects in composite plates using guided wave-based algorithms, such as delay and sum and Excitelet, are presented. Those algorithms are applied to passive data for which the signal corresponding to each emitter-receiver couple is recovered as a result of the cross correlation of the ambient noise measured simultaneously by the two sensors. The transition to passive imaging allows the use of lighter sensors that are unable to emit ultrasonic waves, such as fiber Bragg gratings (FBGs) sensors on optical fibers, which are used in this study. The imaging results presented here show the feasibility of active and passive imaging in composite plates using FBGs as receivers, reducing the impact of the acquisition system on the structure in the context of structural health monitoring.
ABSTRACT
The paper presents a Compressed Sensing technique for the reconstruction of guided wavefields. Structural inspections based on the analysis of guided wavefields have proven to be effective at detecting and characterizing damage. However, wavefield detection is often a time consuming process, which limits practicality. The proposed reconstruction technique estimates the location of sources and structural features interacting with the waves from a set of sparse measurements. Such features include damage, described as a scattering source. The wavefield is reconstructed by employing information on the dispersion properties of the medium under consideration. The procedure is illustrated through a one-dimensional analytical example, and subsequently applied to the reconstruction of an experimental wavefield in a composite panel with an artificial delamination. The results confirm the ability of the technique to identify the defect, while reconstructing the wavefield with good accuracy using a significantly reduced number of measurements.
