Lamb wave defect detection and evaluation using a fully non-contact laser system

Traditional Lamb wave structural health monitoring (SHM)/nondestructive evaluation (NDE) system employs contact type transducers such as PZT, ultrasonic transducers, and optical fibers. In application, transducer attachment and maintenance can be time and labor consuming. In addition, the use of couplant and adhesives can introduce additional materials on structures, and the interface coupling is often not well understood. To overcome these limitations, we proposed a fully non-contact NDE system by employing pulsed laser (PL) for Lamb wave actuation and scanning laser Doppler vibrometer (SLDV) for Lamb wave sensing. The proposed system is implemented on aluminum plates. The PL Lamb wave excitation is calibrated, and the optimal parameters are obtained. Lamb wave modes are then characterized through 1D wavefield analysis. With the calibrated and characterized system, defect detection and evaluation are achieved on aluminum plates with simulated defects (surfaced-bonded quartz rod, and machine milled crack) through 1D and 2D inspection in both time-space and frequency-wavenumber domains

Piezoelectric Wafer Active Sensors in Lamb Wave-Based Structural Health Monitoring

Recent advancements in sensors and information technologies have resulted in new methods for structural health monitoring (SHM) of the performance and deterioration of structures. The enabling element is the piezoelectric wafer active sensor (PWAS). This paper presents an introduction to PWAS transducers and their applications in Lamb wave-based SHM. We begin by reviewing the fundamentals of piezoelectric intelligent materials. Then, the mechanism of using PWAS transducers as Lamb wave transmitters and receivers is presented. PWAS interact with the host structure through the shear-lag model. Lamb wave mode tuning can be achieved by judicious combination of PWAS dimensions, frequency value, and Lamb mode characteristics. Finally, use of PWAS Lamb wave SHM for damage detection on plate-like aluminum structures is addressed. Examples of using PWAS phased array scanning, quantitative crack detection with array imaging, and quantitative corrosion detection are given

Multi-site Delamination Detection and Quantification in Composites through Guided Wave Based Global-local Sensing

Advanced composite materials are contributing to a revolution in aerospace applications. Rapid inspection techniques for detecting and quantifying damage in large composites are critical for ensuring operability and safety of composite structures. Moreover, in the development and manufacturing of next-generation composite materials, rapid inspection techniques are imperative for evaluating and certifying the materials. This paper presents a guided wave based global-local sensing method for rapid detection and quantification of delamination damage in large composite panels. The global-local approach uses a hybrid system consisting of a piezoelectric transducer (PZT) for generating guided waves and a non-contact scanning laser Doppler vibrometer (SLDV) for acquiring guided wave data. The global-local inspection is performed in two steps. First, a phased array configured of a small number of SLDV scan points (for example 10×10 points in a rectangular grid array) performs inspection over the entire plate to detect and locate damage (Figure 1a). Local areas are identified as potential damage regions for the second step. Then high density wavefield measurements are taken over the target damage areas and wavefield analysis is performed to quantitatively evaluate the damage (Figure 1b). For the proof of concept, the global-local approach is demonstrated on a carbon fiber reinforced polymer (CFRP) composite plate with two sites of impact-induced delamination damage. In the first step, the locations of two delamination sites are detected by the phased array method. In the second step, the delamination size and shape are evaluated through the wavefield analysis. The detected delamination location, size and shape agree well with those of ultrasonic C-scan

Structural health monitoring apparatus and methodology

Disclosed is an apparatus and methodology for structural health monitoring (SHM) in which smart devices interrogate structural components to predict failure, expedite needed repairs, and thus increase the useful life of those components. Piezoelectric wafer active sensors (PWAS) are applied to or integrated with structural components and various data collected there from provide the ability to detect and locate cracking, corrosion, and disbanding through use of pitch-catch, pulse-echo, electro/mechanical impedance, and phased array technology. Stand alone hardware and an associated software program are provided that allow selection of multiple types of SHM investigations as well as multiple types of data analysis to perform a wholesome investigation of a structure

3D Guided Wave Motion Analysis on Laminated Composites

Ultrasonic guided waves have proved useful for structural health monitoring (SHM) and nondestructive evaluation (NDE) due to their ability to propagate long distances with less energy loss compared to bulk waves and due to their sensitivity to small defects in the structure. Analysis of actively transmitted ultrasonic signals has long been used to detect and assess damage. However, there remain many challenging tasks for guided wave based SHM due to the complexity involved with propagating guided waves, especially in the case of composite materials. The multimodal nature of the ultrasonic guided waves complicates the related damage analysis. This paper presents results from parallel 3D elastodynamic finite integration technique (EFIT) simulations used to acquire 3D wave motion in the subject laminated carbon fiber reinforced polymer composites. The acquired 3D wave motion is then analyzed by frequency-wavenumber analysis to study the wave propagation and interaction in the composite laminate. The frequency-wavenumber analysis enables the study of individual modes and visualization of mode conversion. Delamination damage has been incorporated into the EFIT model to generate "damaged" data. The potential for damage detection in laminated composites is discussed in the end

Crack Detection with Lamb Wave Wavenumber Analysis

In this work, we present our study of Lamb wave crack detection using wavenumber analysis. The aim is to demonstrate the application of wavenumber analysis to 3D Lamb wave data to enable damage detection. The 3D wavefields (including vx, vy and vz components) in time-space domain contain a wealth of information regarding the propagating waves in a damaged plate. For crack detection, three wavenumber analysis techniques are used: (i) two dimensional Fourier transform (2D-FT) which can transform the time-space wavefield into frequency-wavenumber representation while losing the spatial information; (ii) short space 2D-FT which can obtain the frequency-wavenumber spectra at various spatial locations, resulting in a space-frequency-wavenumber representation; (iii) local wavenumber analysis which can provide the distribution of the effective wavenumbers at different locations. All of these concepts are demonstrated through a numerical simulation example of an aluminum plate with a crack. The 3D elastodynamic finite integration technique (EFIT) was used to obtain the 3D wavefields, of which the vz (out-of-plane) wave component is compared with the experimental measurement obtained from a scanning laser Doppler vibrometer (SLDV) for verification purposes. The experimental and simulated results are found to be in close agreement. The application of wavenumber analysis on 3D EFIT simulation data shows the effectiveness of the analysis for crack detection. Keywords: : Lamb wave, crack detection, wavenumber analysis, EFIT modelin

Phased Array Beamforming and Imaging in Composite Laminates Using Guided Waves

This paper presents the phased array beamforming and imaging using guided waves in anisotropic composite laminates. A generic phased array beamforming formula is presented, based on the classic delay-and-sum principle. The generic formula considers direction-dependent guided wave properties induced by the anisotropic material properties of composites. Moreover, the array beamforming and imaging are performed in frequency domain where the guided wave dispersion effect has been considered. The presented phased array method is implemented with a non-contact scanning laser Doppler vibrometer (SLDV) to detect multiple defects at different locations in an anisotropic composite plate. The array is constructed of scan points in a small area rapidly scanned by the SLDV. Using the phased array method, multiple defects at different locations are successfully detected. Our study shows that the guided wave phased array method is a potential effective method for rapid inspection of large composite structures

Dual Mode Sensing with Low-Profile Piezoelectric Thin Wafer Sensors for Steel Bridge Crack Detection and Diagnosis

Monitoring of fatigue cracking in steel bridges is of high interest to many bridge owners and agencies. Due to the variety of deterioration sources and locations of bridge defects, there is currently no single method that can detect and address the potential sources globally. In this paper, we presented a dual mode sensing methodology integrating acoustic emission and ultrasonic wave inspection based on the use of low-profile piezoelectric wafer active sensors (PWAS). After introducing the research background and piezoelectric sensing principles, PWAS crack detection in passive acoustic emission mode is first presented. Their acoustic emission detection capability has been validated through both static and compact tension fatigue tests. With the use of coaxial cable wiring, PWAS AE signal quality has been improved. The active ultrasonic inspection is conducted by the damage index and wave imaging approach. The results in the paper show that such an integration of passive acoustic emission detection with active ultrasonic sensing is a technological leap forward from the current practice of periodic and subjective visual inspection and bridge management based primarily on history of past performance

Do we really need temporal convolutions in action segmentation?

Action classification has made great progress, but segmenting and recognizing actions from long untrimmed videos remains a challenging problem. Most state-of-the-art methods focus on designing temporal convolution-based models, but the inflexibility of temporal convolutions and the difficulties in modeling long-term temporal dependencies restrict the potential of these models. Transformer-based models with adaptable and sequence modeling capabilities have recently been used in various tasks. However, the lack of inductive bias and the inefficiency of handling long video sequences limit the application of Transformer in action segmentation. In this paper, we design a pure Transformer-based model without temporal convolutions by incorporating temporal sampling, called Temporal U-Transformer (TUT). The U-Transformer architecture reduces complexity while introducing an inductive bias that adjacent frames are more likely to belong to the same class, but the introduction of coarse resolutions results in the misclassification of boundaries. We observe that the similarity distribution between a boundary frame and its neighboring frames depends on whether the boundary frame is the start or end of an action segment. Therefore, we further propose a boundary-aware loss based on the distribution of similarity scores between frames from attention modules to enhance the ability to recognize boundaries. Extensive experiments show the effectiveness of our model