Showing papers in "Journal of Nondestructive Evaluation in 2017"

Non-destructive Investigation of Paintings on Canvas by Continuous Wave Terahertz Imaging and Flash Thermography

Abstract: Terahertz (THz) imaging is increasingly used in the cultural heritage field. In particular, continuous wave (CW) and low frequency THz is attracting more attention. The first application of the THz technique inherent to the cultural heritage field dates back 10 years ago. Since 2006, tangible improvements have been conducted in the refinement of the technique, with the aim to produce clear maps useful for any art restorer. In this paper, a CW THz (0.1 THz) imaging system was used to inspect paintings on canvas both in reflection and in transmission modes. In particular, two paintings were analyzed: in the first one, similar materials and painting execution of the original artwork were used, while in the second one, the canvas layer is slightly different. Flash thermography was used herein together with the THz method in order to observe the differences in results for the textile support materials. A possible application of this method for the detection of artwork forgery requires some parameterization and analysis of various materials or thickness influence which will be addressed in a future study. In this work, advanced image processing techniques including principal component thermography (PCT) and partial least squares thermography (PLST) were used to process the infrared data. Finally, a comparison of CW THz and thermographic results was conducted.

Detection and Characterization of Local Defect Resonances Arising from Delaminations and Flat Bottom Holes

Abstract: The concept of local defect resonance (LDR) can be used to diagnose the presence of defects, such as delaminations and cracks, and enable their localisation. This paper describes an automated detection methodology of LDR frequencies using signals measured by laser Doppler vibrometry. The performance of the detection algorithm is validated, both numerically and experimentally, on an aluminum sample containing a flat bottom hole. Subsequently, the algorithm has been applied on a sample made of glass-fiber-reinforced polymer that contains a single circular delamination and a composite sandwich structure with a disbond. In all cases, the lowest order LDR frequencies were successfully identified.

Imaging of Barely Visible Impact Damage on a Complex Composite Stiffened Panel Using a Nonlinear Ultrasound Stimulated Thermography Approach

Abstract: Thermosonics, also known as ultrasonic stimulated thermography, is a rapid non-destructive evaluation technique that uses an infrared camera to visualise material defects by detecting the frictional heating at crack surfaces when a part under inspection is vibrated These vibrations are usually produced by an ultrasonic horn being pressed against the surface of the test sample, which result in uncontrolled generations of frequency components and excitation amplitude This makes thermosonics highly non-reproducible and unreliable This paper presents a novel thermographic method, here named as nonlinear ultrasound stimulated thermography, for the detection and imaging of real material defects such as impact damage on a complex composite stiffener panel This technique combines nonlinear ultrasonic techniques with thermography A nonlinear ultrasonic approach was used as signature for a reliable frequency-selective excitation of material defects, while an infrared camera was employed to reveal the damage location and severity A nonlinear narrow sweep excitation method was employed to efficiently excite the local resonance frequencies of the damaged region in order to give rise to the highest nonlinear harmonic response in the material leading to a high heat generation at the crack surface The experimental tests were carried out with a laser vibrometer in order to better understand the interaction of elastic waves with nonlinear scattering An ad-hoc nonlinear thermal-structural finite element and crack model was developed to study the heat generation caused by the movement of the crack surfaces when elastic waves with a particular frequency impinges on the crack interphase with good agreement with the experimental results The proposed new method allows to detect single and multiple barely visible impact damage in a quick, reliable and reproducible manner and overcomes the main limitations of classical thermosonics

Embedded PZT Sensor for Monitoring Mechanical Impedance of Hydrating Cementitious Materials

Abstract: An embedded PZT (Lead Zirconate Titanate)-based sensor is developed for real-time, continuous, in-situ monitoring of hydrating cementitious materials after casting. The development of a multi-layer protection for a PZT patch, which provides a physical barrier with the surrounding medium while ensuring the sensitivity of measurement is described. Electrical impedance measurements from the sensor embedded inside mortar mixtures of different compositions are shown to sensitively provide an indication of changes in the state and the mechanical impedance of the material during periods associated with setting and early strength gain. An analytical procedure is developed for extracting the mechanical impedance of the surrounding cementitious material from the electromechanical measurements of the embedded PZT sensor. Changes in the mechanical impedance of mortars through periods of setting and early strength gain obtained from the embedded PZT sensor are validated using pin penetration, isothermal calorimetry and vibration-based measurements. Kinetics of hydration reaction obtained from isothermal calorimetry and increase in the penetration resistance during the setting behavior in the material, are accurately reflected in the increase in the mechanical impedance of the surrounding mortar obtained from the embedded PZT sensor. The continued increase in the mechanical impedance of the mortar after setting, up to 28 days, correlates well with the increase in elastic modulus of material obtained from vibration-based measurements. The durability of the sensor protection scheme is verified by evaluating the performance of sensors recovered from inside the mortar after long-term embedment. The embedded PZT sensor offers the potential for monitoring the local property development in a cementitious material from within the bulk of the structure and for use in quality assessment.

Optimization of the Inspection of Large Composite Materials Using Robotized Line Scan Thermography

Abstract: The emergence of composite materials has started a revolution in the aerospace industry. When using composite materials, it is possible to design larger and lighter components. However, due to their anisotropy, composite materials are usually difficult to inspect and detecting internal defects is a challenge. Line scan thermography (LST) is a dynamic thermography technique, which is used to inspect large components of metallic surfaces and composites, commonly used in the aerospace industry. In this paper, the robotized LST technique has been investigated on a large composite component which contains different types of internal defects located at a variety of depths. For theoretical analysis, the LST inspection was simulated using a mathematical formulation based on the 3D heat conduction equation in the transient regime in order to determine the optimum parameters. The solution of the model was performed using the finite element method. The LST parameters were adjusted to detect the deepest defects in the specimen. In order to validate the numerical results with experimental data, a robotized system in which the infrared camera and the heating source move in tandem, has been employed. From the experimental tests, it was noted that there are three sources of noise (non-uniform heating, unsynchronized frame rate with scanning speed and robot arm vibration) which affect the performance of the test. In this work, image processing techniques that were initially developed to be applied on pulse thermography have been successfully implemented. Finally, the performance of each technique was evaluated using the probability of detection approach.

3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface

Abstract: Three-dimensional surface defect inspection remains a challenging task. This paper describes a novel automatic vision-based inspection system that is capable of detecting and characterizing defects on an airplane exterior surface. By analyzing 3D data collected with a 3D scanner, our method aims to identify and extract the information about the undesired defects such as dents, protrusions or scratches based on local surface properties. Surface dents and protrusions are identified as the deviations from an ideal, smooth surface. Given an unorganized point cloud, we first smooth noisy data by using Moving Least Squares algorithm. The curvature and normal information are then estimated at every point in the input data. As a next step, Region Growing segmentation algorithm divides the point cloud into defective and non-defective regions using the local normal and curvature information. Further, the convex hull around each defective region is calculated in order to englobe the suspicious irregularity. Finally, we use our new technique to measure the dimension, depth, and orientation of the defects. We tested and validated our novel approach on real aircraft data obtained from an Airbus A320, for different types of defect. The accuracy of the system is evaluated by comparing the measurements of our approach with ground truth measurements obtained by a high-accuracy measuring device. The result shows that our work is robust, effective and promising for industrial applications.

Deformation Tracking in 3D Point Clouds Via Statistical Sampling of Direct Cloud-to-Cloud Distances

Abstract: Dense three-dimensional (3D) point clouds of infrastructure systems, generated from laser scanners or through multi-view photogrammetry, have significant potential as a source of nondestructive evaluation information. The growing maturity of these techniques make them capable of reconstructing photorealistic 3D models with accuracy on the millimeter scale, adequate for inspection and evaluation practices. Manual analysis of these point clouds is often time consuming and labor intensive and does not provide explicit information on structural performance and health conditions, highlighting the need for new techniques to efficiently analyze these models. This paper presents a new 3D point cloud change analysis approach for tracking small movements over time through localized spatial analytics. This technique uses a combination of a direct point-wise distance metric in conjunction with statistical sampling to extract structural deformations. By identifying and tracking these changes, mechanical deformations can be quantified along with the associated strains and stresses. These measurements can then be used to assess both service conditions and remaining system capacity. The results of a series of laboratory experiments designed to test the proposed approach are presented as well. The findings indicate measurement accuracy on the order of +/− 0.2 mm (95% confidence interval), making it suitable for accurate and automatic geometrical analyses and change detection in a variety of infrastructure inspection scenarios. Ongoing work seeks to connect this technique to automated finite element model updating, and to field test the measurement technique.

Effect of Defect Size on Subsurface Defect Detectability and Defect Depth Estimation for Concrete Structures by Infrared Thermography

Abstract: This study aims to reveal the effect and correlation of delamination size and defect shape for using infrared thermography (IRT) through FE modeling to enhance the reliability and applicability of IRT for effective structural inspections. Regarding the effect of delamination size, it is observed that the temperature difference between sound and delaminated area ( $$\Delta $$ T) increases as the size of delamination increases; however, $$\Delta $$ T converges to a certain value when the area is 40 $$\times $$ 40 cm and the thickness is 1 cm. As for the shape of delamination, it can be assumed that if the aspect ratio which is the ratio of the length of the shorter side to the longer side of the delamination is more than 25%, $$\Delta $$ T of any delaminations converges to $$\Delta $$ T of the same area of a square/circular-shaped delamination. Furthermore, if the aspect ratio is 25% or smaller, $$\Delta $$ T becomes smaller than the $$\Delta $$ T of the same area of a square/circular-shaped delamination, and it is getting smaller as the ratio becomes smaller. Furthermore, this study attempts to estimate depths of delaminations by using IRT data. Based on the correlation between the size of delamination and the depth from the concrete surface in regard to $$\Delta $$ T, it was assumed that it was possible to estimate the depth of delamination by comparing $$\Delta $$ T from IRT data to $$\Delta $$ T at several depths obtained from FE model simulations. Through the investigation using IRT data from real bridge deck scanning, this study concluded that this estimation method worked properly to provide delamination depth information by incorporating IRT with FE modeling.

Least Square Fitting for Adaptive Wavelet Generation and Automatic Prediction of Defect Size in the Bearing Using Levenberg–Marquardt Backpropagation

Abstract: In this communication, an attempt has been made to develop an algorithm for automatic prediction of the size of the bearing defect during operation of a machine. Features for the purpose are meticulously designed so as defect commencement and termination events in the signal could be easily spotted. Information on commencement of defect in the signal, in general, is very weak. It is enhanced by approximating the burst in the signal to a wavelet, making use of least squares fitting. Levenberg–Marquardt back propagation network is used for prediction of defect size from defect features. The comparison shows that the Levenberg–Marquardt back propagation network outperforms another network in terms of accuracy. The experimental validation of the proposed scheme is carried out for four different defect sizes each for the inner race, outer race, and roller defect. The maximum deviation in the width measurement result is 5.35% which occurs in the case of bearing with roller defect of width 1.12 mm. The performance evaluation of the method is also carried out using t test. The result of t test validates the accuracy of proposed method in the prediction of defect width.

A Novel Non-destructive Testing Method by Measuring the Change Rate of Magnetic Flux Leakage

Abstract: The application of magnetic sensors in the traditional magnetic flux leakage (MFL) technique has a significant influence on the detection results. The sensor is typically used to directly measure the amplitude of the magnetic leakage flux intensity as the detection signal. In view of noise effects on the detection result and the subsequent misinterpretation of defect signals, a new non-destructive testing method is proposed. The proposed method intends to measure the magnetic flux change rate using two sensors. A mathematical model is first established to present the principle of the change rate measurement. Based on the magnetic dipole theory, it is inferred that the new method is applicable and sensitive to the detection and location of defects. Moreover, this method is advantageous as it inhibits the interference of MFL noises such as the distension noise, background noise, and vibration noise. The model predictions are then verified by a series of simulations. Finally, an experimental platform is set up to practically detect the defect of a steel plate, and the results agree with the demonstrations and simulations.

Model Assisted Probability of Detection Curves: New Statistical Tools and Progressive Methodology

Abstract: The probability of detection curve is a standard tool in several industries to evaluate the performance of non destructive testing (NDT) procedures for the detection of harmful defects for the inspected structure. Due to new capabilities of NDT process numerical simulation, model assisted probability of detection (MAPOD) approaches have also been recently developed. In this paper, a generic and progressive MAPOD methodology is proposed. Limits and assumptions of the classical methods are enlightened, while new metamodel-based methods are proposed. They allow to access to relevant information based on sensitivity analysis of MAPOD inputs. Applications are performed on eddy current non destructive examination numerical data.

Induction Hardened Layer Characterization and Grinding Burn Detection by Magnetic Barkhausen Noise Analysis

Abstract: The quality of the ball screw shafts used in the aeronautical sector has to be controlled and certified with the most advanced non-destructive techniques. The capacity of magnetic Barkhausen noise (MBN) as a non-destructive technique to control the quality of ball screw shafts by assuring the appropriate induction hardened layer depth and detecting local overheated regions, known as grinding burns, which may occur during grinding processes is shown in the present work. Magnetic Barkhausen noise measurements were made with a system designed and implemented by the authors and the derived parameters were compared with microhardness measurements made at various depths after the different induction hardening treatments and the grinding processes were applied. A multiparametric study of the MBN signal as a function of the magnetic field in the surface of the sample is done in order to estimate the thickness of the hardened layer and to detect the grinding burns produced during grinding processes. The hardened layer thickness can be characterized with an error of ±200 \(\upmu \)m in the range between 150 and 2500 \(\upmu \)m by the position of the first peak of the MBN envelope in terms of the tangential magnetic field measured at the surface and the grinding burns can be detected with the position of the second peak of the MBN envelope in terms of the tangential magnetic field measured at the surface.

Simulation of Laser Ultrasonics for Detection of Surface-Connected Rail Defects

Abstract: Laser ultrasonic produces frequencies in the MHz range, enabling high accuracy and a strong ability to detect rail surface defects. This paper mainly studied on the simulation of detecting surface-connected rail defects on 60 kg rails with laser ultrasonic, established the finite element model of laser-excited ultrasonic Rayleigh wave, carried out the simulation, and verified the effectiveness of the technology through experiments. To solve the problem that laser ultrasonic is insensitive to the width of defects in actual detection, and unable to make quantitative detection of defects, this paper established a new model on the basis of improving the original model that has been verified, exciting ultrasonic at the two sides at the same time of a rail with two staggered beams of laser separately to detect irregular scratch defects on rail surface, and two groups of signal data were received through two probes. Each group of data can present the half-profile information of defects, and further form two detection images of the defect. At last, the two detection images were combined into a complete image through image processing. The results of the experiment indicate that the technology studied offers a new method for the effective quantitative detection of surface-connected defects on rail.

A Permeability-Measuring Magnetic Flux Leakage Method for Inner Surface Crack in Thick-Walled Steel Pipe

Abstract: It is difficult for traditional magnetic flux leakage (MFL) methods to detect inner surface cracks of thick-walled steel pipe or plate due to magnetic shielding of the wall and strong magnetic background noise, and for eddy current testing (ECT) as well due to its skin effect. On the basis of the nonlinear magnetic permeability of ferromagnetic materials, a new non-destructive testing method (NDT) permeability-measuring magnetic flux leakage (P-MFL) is proposed, in which the magnetization is perpendicular to the inner surface crack, and the surface layer permeability distortion caused by magnetic field distortion is measured by differential pick-up coils. Afterwards, its detection mechanism is presented and analyzed, and its feasibility is verified by simulations and experiments. Finally, some application cases for steel pipe are also realized effectively. Meanwhile, its testing characteristics for cracks are given and effects of crack size, specimen thickness, scanning paths to testing signal amplitude are briefly analyzed. Finally, the proposed P-MFL method compared to traditional MFL method is discussed in detail.

Prediction of the Mechanical Properties of P91 Steel by Means of Magneto-acoustic Emission and Acoustic Birefringence

Abstract: The paper describes an application of non-destructive volumetric magnetic and ultrasonic techniques for evaluation of the selected mechanical parameter variations of P91 steel having direct influence on its suitability for further use in critical components used in power plants. Two different types of deformation processes were carried out. First, a series of the P91 steel specimens was subjected to creep and second, one to plastic deformation in order to achieve the material with an increasing strain level up to 10%. Subsequently, non-destructive and destructive tests were performed. Magnetic methods based on measurements of magnetoacoustic emission and magnetic hysteresis loop changes as well as the ultrasonic method based on acoustic birefringence measurements, were applied. Finally, the static tensile tests were carried out in order to evaluate the mechanical parameters. It is shown that some relationships between the selected parameters coming from the non-destructive and destructive tests may be formulated.

Impact-Based Nonlinear Acoustic Testing for Characterizing Distributed Damage in Concrete

Abstract: Nonlinear acoustics-based nondestructive evaluation (NDE) techniques have shown great promise for identification of microstructure and microcracking in a wide spectrum of materials (eg, metals, metallic alloys, composites, rocks, cementitious materials) This class of NDE techniques relies on measuring nonlinearity parameters by analyzing the acoustic response of materials that are dynamically perturbed at microstrain levels (strain $$\sim $$ 10 $$^{-6}$$ –10 $$^{-5})$$ Using a mechanical impact to induce microstrain is advantageous for concrete testing because it allows for testing of larger concrete specimens offering potential field transportability In this paper, two impact-based nonlinear acoustic testing techniques are compared: impact-based nonlinear resonant acoustic spectroscopy (INRAS) and dynamic acousto-elastic testing (IDAET) INRAS gives a global measure of sample hysteretic nonlinearity while IDAET provides a local but comprehensive account of nonlinear elastic properties We discuss single- versus multi-impact INRAS and propose a physics-based model to describe the data from single-impact INRAS Then, we introduce IDAET and demonstrate how to extract both classical and non-classical nonlinear parameters from a limited set of test results INRAS and IDAET are used to monitor the evolution of damage in two sets of concrete samples undergoing freeze-thaw (FT) cycles Nonlinear parameters extracted from the two tests show good agreement; all exhibiting far more sensitivity to distributed FT damage than standard (ie linear) resonance frequency measurements By presenting alternative ways to collect and analyze the impact-based nonlinear acoustic test data, this study will help in broadening their use and extending their applications to quantitative in-situ evaluation

Inspecting Marquetries at Different Wavelengths: The Preliminary Numerical Approach as Aid for a Wide-Range of Non-destructive Tests

Abstract: The present study is based on the non-destructive inspection of two marquetries representing arms’ coats, which were produced by the Technical University in Zvolen (Slovakia) and tested under laboratory conditions. The aforesaid samples were made of traditional European and exotic wood species, while the veneers of the decorative layers were prepared through the technology cutting technique, emphasizing in such a manner the wooden texture. One sample was a defect-free panel, while the second one consisted of three sub-superficial flaws and one superficial putty insert, added during the manufacturing stage. The samples were inspected by different non-destructive techniques, such as visible imaging, ultraviolet testing, near-infrared reflectography and transmittography, infrared thermography, holographic interferometry, digital image correlation, laser speckle contrast imaging and ultrasonic testing. Sometimes a comparison was not performed, by avoiding unnecessary data processing. Numerical simulations focusing on the optimization of the provided thermal flux anticipated the experimental results. The latter analysis proved the necessity for the integration of experimental and numerical testing in similar case studies. A peculiarity of this work is the additional creation of an ad hoc Matlab\(^\circledR \) code, written under the LSCI conditions, which identifies the wooden texture. The interactive methodology applied in the present study verified the synergy of the selected inspection methods enabling the production of a complete view for the preservation state of the inspected marquetry samples, through the comparison and/or the correlation of the individual informative content produced by each inspection procedure.

The Fuzzy Logic Application in Volume Fractions Prediction of the Annular Three-Phase Flows

Abstract: In this paper, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system consisting of $$^{152}$$ Eu and $$^{137}$$ Cs and one NaI detector, and then modeled by fuzzy logic. Since the summation of volume fractions are constant (equal to 100%), therefore the fuzzy network must predict only two volume fractions. In this study, three fuzzy networks are applied. The first network is utilized to predict the gas and water volume fractions. The next one is applied to predict the gas and oil volume fractions, and the last one to predict the water and oil volume fractions. In the next step, the numerically obtained data from MCNP-X code, must be imported to the fuzzy models. Then, the average errors of these three networks are computed and compared. The network which has the least error is selected as the best predictor model. According to the modeling results, the best fuzzy network, predicts the gas and water volume fractions with the mean relative error of less than 0.3%, which shows that the fuzzy logic can predict the results precisely.

Assessing the Scaling Subtraction Method for Impact Damage Detection in Composite Plates

Abstract: The scaling subtraction method (SSM) is a non-destructive measurement approach used to extract nonlinear features from the elastic response of a structure. As such it can be used for damage detection purposes by identifying nonlinearities that may result from the presence of micro cracks or inclusions in granular and metallic materials. The effectiveness of such a technique to detect the presence of damage modes typical of laminated composite materials has not been yet assessed. With the purpose of filling this gap, in this paper the SSM is applied to inspect two laminated composite plates with different sizes, impact positions and sensor arrangement. Intact and damaged specimens are tested under harmonic excitations of different amplitude and frequency (the latter selected among the ultrasonic natural frequencies of the two plates). For each excitation case the recorded vibration signals are subtracted from the linearly rescaled reference signals and the SSM nonlinear indicators are calculated. The sensitivity of the method to the presence of damage is assessed in different sensor-receiver scenarios as well as for different excitation frequency and amplitude levels. Finite element numerical investigations are also performed to make comparisons with the experimental results.

Experimental Investigation of Notch-Type Damage Identification with a Curvature-Based Method by Using a Continuously Scanning Laser Doppler Vibrometer System

Abstract: This paper experimentally investigates a notch-type damage identification methodology for beams by using a continuously scanning laser Doppler vibrometer (CSLDV) system. Velocity response of a beam along a scan line under sinusoidal excitation is measured by the CSLDV system and an operating deflection shape (ODS) of the beam is obtained by the demodulation method from velocity response. The ODS of an associated undamaged beam is obtained by using a polynomial with a proper order to fit the ODS from the demodulation method. The curvature of an ODS (CODS) can be calculated with a high quality due to a dense measurement grid of the ODS. A curvature damage index (CDI) is proposed to identify a notch with a length of 1 mm along a beam and a depth of 0.9 mm under different excitation frequencies. The CDI uses differences between CODSs associated with ODSs that are obtained by the demodulation method and the polynomial fit; an auxiliary CDI obtained by averaging CDIs at different excitation frequencies is defined to further assist identification of damage. An averaging technique is applied to velocity response of the beam to reduce measurement noise. Effects of the number of averages on ODSs, CODSs, and CDIs are investigated. Four scan lines with an equal length of 151 mm and different locations with respect to the notch are used to investigate reliability of the proposed methodology. Finally, a whole scan line with a length of 555 mm along the beam is applied and the notch is successfully identified near regions with consistently high values of CDIs at different excitation frequencies; it can also be identified with the auxiliary CDI by a prominent peak at the location of the notch.

Determination of Crack Surface Orientation in Carbon Fibre Reinforced Polymers by Measuring Electromagnetic Emission

Abstract: Electromagnetic emission (EME) generated by fracture of carbon fibre reinforced polymers (CFRP) is studied. The fracture is induced to cross-ply CFRP by mechanical loading in a three-point bending configuration. An EME acquisition set-up operating on the principle of capacitive coupling is used to measure the low frequency (kHz-MHz range) electric field whose generation is attributed to the charge redistribution accompanying the fracture processes. Multiple, differently oriented EME sensors, for the simultaneous EME measurement with different source-sensor orientations, were applied to account for the directionality of the EME sources and their generated electric fields. A method to deduce the crack orientation based on the emitted EME field’s directionality is proposed. A comparison between the angles of the EME sources obtained by this method and the actual crack surface orientations as determined by computed tomography is made.

Optimizing the Inversion Protocol to Determine the Geometry of Vertical Cracks from Lock-in Vibrothermography

Abstract: We use low intensity lock-in vibrothermography to characterize vertical cracks of any shape. The inverse problem, consisting in finding the geometry and location of the crack from vibrothermography data is ill-posed, which makes it necessary to stabilize the inversion algorithm. In previous works, we developed a stabilized inversion procedure that was able to characterize vertical cracks of rectangular shape from lock-in vibrothermography data. In this work, we extend the method to characterize vertical cracks of arbitrary shape. For this purpose, first we improve the stabilization algorithm to obtain accurate crack shape reconstructions. Then, we optimize the protocol followed to handle data before entering the algorithm. To validate the new procedure we invert both, synthetic data with added uniform noise and experimental data on samples containing calibrated artificial cracks of different shapes. The results show that very accurate reconstructions are obtained for shallow cracks. As the depth of the crack increases, the depth is precisely obtained although the shape of the crack is rounded and its area is slightly overestimated.

Estimation of Crack Depth in Concrete Using Diffuse Ultrasound: Validation in Cracked Concrete Beams

Abstract: The objective of this paper is to experimentally validate the previously proposed diffuse ultrasonic technique for measuring crack depth in concrete structures. The validation ultrasonic measurements are performed on real cracks that are formed in full scale reinforced concrete beams. Three reinforced concrete beams are designed and manufactured, and then placed under four point bending to create vertical cracks on the top surface of the beams. Diffuse ultrasonic measurements are conducted on these cracks using the procedure and instrumentation developed in the previous research. The measured crack depth results are compared with the results of side-surface visual inspection and direct stereo-microscopy measurements on cores extracted from the cracked areas. In all cases considered, the crack depths measured by the diffuse ultrasonic method appear to deviate by about 1 cm when compared to those of the core results. The possible causes of this deviation are discussed. However, the overall results show that the diffuse ultrasonic method provides consistent and reasonably accurate crack depth measurement in concrete beams.

Method for Removing Secondary Peaks in Remote Field Eddy Current Testing of Pipes

Abstract: In the remote field eddy current (RFEC) testing of pipes, because the remote eddy current penetrates the pipe’s wall twice, the testing results exhibit two peaks (primary peak and secondary peak) that originate from both the transmitter and receiver passing by the same place in the pipe. The secondary peaks have the same features as the primary peaks that are used to assess defects, and if there is no separation between primary peaks and secondary peaks, incorrect evaluations of defects will be obtained. Considering the benefits of removing secondary peaks in RFEC testing, dual receivers are taken into account. Dual receivers are set in remote fields and are set coaxially to the transmitter to obtain differential signals at the same time. In the proposed method, position dependent response of the differential signals from the dual receivers is calibrated, a Wiener deconvolution filter is used to identify secondary peaks and filter testing noise, and the factors that affect results of removing secondary peaks are also analyzed. To validate the feasibility of the proposed method of RFEC testing, ANSYS is made use of when setting up the analysis model, and an experimental pipe is designed to be identical to ANSYS model. The results of the analysis of ANSYS and experiments both validate the practicality of the proposed method and show the benefits of simplifying the analysis of RFEC signals.

Measuring Alkali-Silica Reaction (ASR) Microscale Damage in Large-Scale Concrete Slabs Using Nonlinear Rayleigh Surface Waves

Abstract: The second harmonic generation (SHG) of nonlinear Rayleigh waves is applied to characterize the damage state induced by alkali-silica reaction (ASR) in large-scale plain concrete slabs. A measurement setup that uses a wedge transmitter and a non-contact, air-coupled receiver is implemented to generate and detect nonlinear Rayleigh surface waves and to obtain both the linear and nonlinear acoustic parameters, while complementary measurements track expansion of the concrete slabs. These results demonstrate the potential of SHG using nonlinear Rayleigh waves to assess the evolution of microscale damage induced by ASR in large-scale, in-service concrete components.

Threat Objects Detection in X-ray Images Using an Active Vision Approach

Abstract: X-ray testing for baggage inspection has been increasingly used at airports, reducing the risk of terrorist crimes and attacks. Nevertheless, this task is still being carried out by human inspectors and with limited technological support. The technology that is being used is not always effective, as it depends mainly on the position of the object of interest, occlusion, and the accumulated experience of the inspector. Due to this problem, we have developed an approach that inspects X-ray images using active vision in order to automatically detect objects that represent a threat. Our method includes three steps: detection of potential threat objects in single views based on the similarity of features and spatial distribution; estimation of the best-next-view using Q-learning; and elimination of false alarms based on multiple view constraints. We tested our algorithm on X-ray images that included handguns and razor blades. In the detection of handguns we registered good results for recall and precision (Re = 67%, Pr = 83%) along with a high performance in the detection of razor blades (Re = 82%, Pr = 100%) taking into consideration 360 inspections in each case. Our results indicate that non-destructive inspection actively using X-ray images, leads to more effective object detection in complex environments, and helps to offset certain levels of occlusion and the internal disorder of baggage.

Application of Different X-ray Techniques to Improve In-Service Carbon Fiber Reinforced Rope Inspection

Abstract: Carbon fiber reinforced polymer ropes are gaining in significance in the fields of civil engineering and hoisting applications. Thus, methods of non-destructive testing (NDT) need to be developed and evaluated with respect to new challenges and types of defects. Particularly important is the development of in-service testing solutions which allow the integration in global online monitoring systems. Conventional methods like electrical resistivity or strain measurements using optical fibers are already in use. This study investigates the possibility of using various X-ray techniques to increase the reliability and significance of NDT and their applicability to in-service testing. Conventional film radiography is the most common technique; however, even after image enhancement of the digitized film, this technique lacks contrast sensitivity and dynamic range compared to digital detector array (DDA) radiography. The DDA radiography is a highly sensitive method; yet, the limitation is that it delivers 2D images of 3D objects. By the use of co-planar translational laminography the detectability of planar defects is superior to 2D methods due to multiple projection angles. Apart from this, it can be used on-site due to a rather simple setup and robust equipment. In this work two photon counting detectors (PCD) with different sensor materials (Si and CdTe) were used. The results show that the resolution and defect recognition is lower in case of DDA radiography and laminography using PCDs compared to high-resolution computed tomography. However, the DDA radiography and laminography are sensitive enough to both fiber breakage and delaminations and can be significantly advantageous in terms of measurement time and adaptability for on-site monitoring.

Investigation of Metal Magnetic Memory Signals of Welding Cracks

Abstract: Metal magnetic memory (MMM) testing is an advanced and nondestructive testing method based on magneto-mechanical effects. This technique can be used to assess defects based on MMM signals (MMMS). However, a thorough understanding of the impact that crack depth, working load, and heat treatment have on residual magnetic field variations (which can in turn affect defect assessment) has not yet been established in the literature. This report presents MMMS testing results of buried welding cracks under different applied loads, as well as before and after heat treatment, in order to analyse the impact of such factors on MMMS data. The results show that the MMMS is significantly affected by the depth at which a given crack is buried in a material, any load applied to the specimen, and the type of heat treatment employed. Therefore, the above factors should be considered when evaluating defects to avoid the inappropriate assessment of such defects.

Using Electromechanical Impedance and Extreme Learning Machine to Detect and Locate Damage in Structures

Abstract: The main objective in structural health monitoring is to keep track of the changes in the dynamic characteristics of the structural system in order both to detect and locate the damage, and to make a decision automatically whether the damage is in dangerous level for the structure or not. In particular, electromechanical impedance (EMI) techniques give simple and low cost solutions for detecting damage in different structures. When it is question of damage localization, the simple analysis of the EMIs fails to furnish enough information. In this paper, an extreme learning machine (ELM) based algorithm is developed for estimating the damage location by using piezoelectric sensors data. The model is trained on simulation generated data and tested on experiments for estimating the damage location by using piezoelectric sensors data. The work’s numerical results have been confirmed either experimentally using laboratory equipment or by employing results available in the open literature and a good agreement has been observed. Experimental results show that ELM can be used as a tool to predict of a single damage in structures. An overall accuracy of 84.5% is achieved with best accuracy of 95%.

Weldment Nondestructive Testing Using Magneto-optical Imaging Induced by Alternating Magnetic Field

Abstract: This paper introduces an innovative Nondestructive testing (NDT) approach by using dynamic magneto-optical imaging (MOI) system to diagnose weld defects. MOI mechanism was explained by Faraday magneto-optical effect and magnetic domain theory. Two Q235 specimen MOI experiments based on excitation of permanent magnet and alternating electromagnet (alternating current driven electromagnet) were performed, thus the feasibility of MOI system for weld defects detection was verified and the relation between alternating magnetic field (AMF) and dynamic MO images was discussed as well. In this research, AMF of welded high-strength steel (HSS) weldment was excited by an alternating electromagnet, and dynamic MO images of HSS seam were acquired for weldment NDT. Finally, a pattern recognition method including three steps was proposed. Dynamic MO images were fused periodically and the features of fused images were extracted by principal component analysis. A classifier based on error back propagation (BP) neural network was established to identify these weld features. It proved that typical weld features such as incomplete penetration, sag, crack and no defect can be classified by the proposed method with an accuracy of 93.5%.