Detection Of Subsurface Defects Research Articles

Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure

Initial defects, for example, those occurring during the production of a rotor blade, encourage early damages such as rain erosion at the leading edge of wind turbine rotor blades. To investigate the potential that initial defects have for early damage, long-pulse thermography as a non-destructive and contactless measurement technique is applied to a strongly curved and coated test specimen for the first time. This specimen is similar in structural size and design to a rotor blade leading edge and introduced with sub-surface defects whose diameters range between 2mm and 3.5mm at depths between 1.5mm and 2.5mm below the surface. On the curved and coated test specimen, sub-surface defects with a depth-to-diameter ratio of up to 1.04 are successfully detected. In particular, defects are also detectable when being observed from a non-perpendicular viewing angle, where the intensity of the defects decreases with increasing viewing angle due to the strong surface curvature. In conclusion, long-pulse thermography is suitable for the detection of sub-surface defects on coated and curved components and is therefore a promising technique for the on-site application during inspection of rotor blade leading edges.

Enhancement of Time Resolution in Ultrasonic Time-of-Flight Diffraction Technique With Frequency-Domain Sparsity-Decomposability Inversion (FDSDI) Method.

The lack of time resolution restricts the quantitative detection of shallow subsurface defects with ultrasonic time-of-flight diffraction (TOFD) technique due to the superposition between lateral wave and diffracted waves from upper and lower tips. In this article, the frequency-domain sparsity-decomposability inversion (FDSDI) method was proposed to enhance the time resolution in TOFD based on the sparsity and decomposability of the ultrasonic reflection sequence. An optimization problem was formulated in the frequency domain by combining l1 - and l2 -norm constraints. The simulation was performed with a carbon steel model containing a series of shallow subsurface cracks at the depths of 2.0, 2.5, 3.0, 3.5, and 4.0 mm. The relative measurement errors of defect depths and heights were no more than 6.57%, and the depth of the dead zone was reduced by 70%. Subsequently, the feasibility of the FDSDI method was experimentally verified on a carbon steel specimen with an artificial defect. The defect depth and height were calculated with relative errors within 6.0%. Finally, the detection capacity of the FDSDI method was discussed, and the effects of frequency bandwidth, regularization parameter, and noise on inversion results were analyzed by experiments. It is concluded that the FDSDI method decouples the multiple overlapped signals and significantly improves the time resolution to quantify the small defects in the dead zone.

Detection of Defects in Geomembranes Using Quasi-Active Infrared Thermography.

High-density polyethylene geomembranes are employed as covers for the sewage treatment lagoons at Melbourne Water Corporation’s Western Treatment Plant, to harvest the biogas produced during anaerobic degradation, which is then used to generate electricity. Due to its size, inspecting the cover for defects, particularly subsurface defects, can be challenging, as well as the potential for the underside of the membrane to come into contact with different substrates, viz. liquid sewage, scum (consolidated solid matter), and biogas. This paper presents the application of a novel quasi-active thermography inspection method for subsurface defect detection in the geomembrane. The proposed approach utilises ambient sunlight as the input thermal energy and cloud shading as the trigger for thermal transients. Outdoor laboratory-scale experiments were conducted to study the proposed inspection technique. A pyranometer was used to measure the intensity of solar radiation, and an infrared thermal camera was used to measure the surface temperature of the geomembrane. The measured temperature profile was analysed using three different algorithms for thermal transient analysis, based on (i) the cooling constant from Newton’s law of cooling, (ii) the peak value of the logarithmic second derivative, and (iii) a frame subtraction method. The outcomes from each algorithm were examined and compared. The results show that, while each algorithm has some limitations, when used in combination the three algorithms could be used to distinguish between different substrates and to determine the presence of subsurface defects.

An Automated Pipeline for Dynamic Detection of Sub-Surface Metal Loss Defects across Cold Thermography Images.

Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion scanning the test piece while cold stimulation is in operation. Immediately after that, the camera registers the thermal evolution. However, thermal reflections, non-uniform stimulation and lateral heat diffusions will remain as undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no prior knowledge of the non-defective area in order to effectively distinguish between defective and non-defective areas. In this work, the previously automated acquisition and processing pipeline is re-designed and optimised for two purposes: 1—Through the previous work, the mentioned pipeline was used to analyse a specific area of the test piece surface in order to reconstruct the reference area and identify defects. In order to expand the application of this device over the entire test area, regardless of its extension, the pipeline is improved in which the final surface image is reconstructed by taking into account multiple segments of the test surface. The previously introduced pre-processing method of Dynamic Reference Reconstruction (DRR) is enhanced by using a more rigorous thresholding procedure. Principal Component Analysis (PCA) is then used in order for feature (DRR images) reduction. 2—The results of PCA on multiple segment images of the test surface revealed different ranges of intensities across each segment image. This potentially could cause mistaken interpretation of the defective and non-defective areas. An automated segmentation method based on Gaussian Mixture Model (GMM) is used to assist the expert user in more effective detection of the defective areas when the non-defective areas are uniformly characterised as background. The final results of GMM have shown not only the capability of accurately detecting subsurface metal loss as low as 37.5% but also the successful detection of defects that were either unidentifiable or invisible in either the original thermal images or their PCA transformed results.

Non-Contact Inspection of Railhead via Laser-Generated Rayleigh Waves and an Enhanced Matching Pursuit to Assist Detection of Surface and Subsurface Defects.

Laser ultrasonic technology can provide a non-contact, reliable and efficient inspection of train rails. However, the laser-generated signals measured at the railhead are usually contaminated with a high level of noise and unwanted wave components that complicate the identification of defect echoes in the signal. This study explores the possibility of combining laser ultrasonic technology (LUT) and an enhanced matching pursuit (MP) to achieve a fully non-contact inspection of the rail track. A completely non-contact laser-based inspection system was used to generate and sense Rayleigh waves to detect artificial surface horizontal, surface edge, subsurface horizontal and subsurface vertical defects created at railheads of different dimensions. MP was enhanced by developing two novel dictionaries, which include a finite element method (FEM) simulation dictionary and an experimental dictionary. The enhanced MP was used to analyze the experimentally obtained laser-generated Rayleigh wave signals. The results show that the enhanced MP is highly effective in detecting defects by suppressing noise, and, further, it could also overcome the deficiency in the low repeatability of the laser-generated signals. The comparative analysis of MP with both the FEM simulation and experimental dictionaries shows that the enhanced MP with the FEM simulation dictionary is highly efficient in both noise removal and defect detection from the experimental signals captured by a laser-generated ultrasonic inspection system. The major novelty contributed by this research work is the enhanced MP method with the developments of, first, an FEM simulation dictionary and, second, an experimental dictionary that is especially suited for Rayleigh wave signals. Third, the enhanced MP dictionaries are created to process the Rayleigh wave signals generated by laser excitation and received using a 3D laser scanner. Fourth, we introduce a pioneer application of such laser-generated Rayleigh waves for inspecting surface and subsurface detects occurring in train rails.

Sub-surface metal loss defect detection using cold thermography and dynamic reference reconstruction (DRR)

Corrosion is considered a destructive phenomenon that affects almost all metals. Active infrared thermography is an online (no result delay) and non-intrusive (no process disruption) method of non-destructive testing (NDT), which has shown profound capabilities of detecting sub-surface metal loss. However, thermal reflections, non-uniform stimulation and lateral heat diffusion will remain as the most undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no a priori knowledge of the anomalies to effectively distinguish between defective and non-defective areas. In this work, cooling stimulation is considered as the thermal excitation mean as 1- a very few reports in this regard have been mentioned in the body of literature and 2- a dynamic setup was achieved that is found to be effective to minimise the possibility of disrupting reflections or artefacts registered by thermal camera similar to the case of using heating stimulation. A state-of-the-art prototype mechanism was manufactured. This equipment includes a carrier carrying a thermal camera and a cooling medium reservoir operating in reciprocating motion setup. This equipment is able to scan the test piece while cold stimulation is in operation, and immediately after that the camera registers the thermal evolution. An automated contrast enhancement pipeline using a variation of adaptive histogram equalisation (AHE) combined with principal component analysis (PCA) method was developed. The enhanced image results demonstrated the capability of accurately detecting sub-surface metal loss as low as 37.5% as well as an efficiently reconstructed reference (non-defective) area.

GPR-RCNN: An Algorithm of Subsurface Defect Detection for Airport Runway Based on GPR

Detection of subsurface defects is important for maintaining runway structural health and reliability. A potential solution is to employ a robot equipped with a Ground Penetrating Radar (GPR) to perform subsurface scanning. To automate the inspection process, we develop a subsurface defect detection algorithm which is a deep learning algorithm that fuses 2D planar features in each panel in GPR B-scans and 3D voxel-wise features in GPR C-scan to robustly detect regions with defects even in the presence of significant noises. Named as GPR-RCNN, we have tested our algorithm with real airport runway data collected from three international airports using our runway inspection robot. The experimental results show that our proposed GPR-RCNN achieves superior results when comparing to state-of-the-art techniques. Specifically, our method achieves F1-measures at 62%, 33%, 81%, and 87% for void, crack, subsidence and pipe, respectively.

Automatic three-dimensional reconstruction of subsurface defects by segmenting ultrasonic point cloud

Ultrasonic inspection is a widely accepted nondestructive method for subsurface defect detection of materials. In the past years, a number of signal processing techniques have been adopted to improve defect visibility. However, an automatic identification method that reconstructs the three-dimensional (3D) shapes of the defects from the ultrasonic data is still desired. In this work, a 3D minimum spanning tree (3D-MST) method is proposed, which treats the ultrasonic data as a point cloud and segment it by finding a tree whose sum of edge weights is as small as possible. The segmentation results give a clear indication of the positions, sizes, and shapes of the subsurface defects contained in the materials. It is also discussed that the segmentation results can be improved by adopting robust normalization as a pretreatment step before conducting 3D-MST. The feasibility of the proposed method is illustrated by case studies on a polymer composite and a food product.

Constrained Autoencoder-Based Pulse Compressed Thermal Wave Imaging for Sub-Surface Defect Detection

Non-destructive testing & evaluation techniques play an essential role in ensuring safety of materials in operation at various industry sectors. Pulse compressed favourable thermal wave imaging is one of the widely used non-destructive testing techniques due to its excellent noise rejection capabilities. However, the high dimensional thermal imaging data needs to be encoded into lossless compressed form to highlight the hidden defects inside the materials. This paper proposes a novel constrained and regularized autoencoder based thermography approach for sub-surface defect detection in a mild steel specimen. Certain properties such as non-correlation of encoded data, weight orthogonality, and weights with unit norm length have been highlighted which are non-existent in linear autoencoders but are responsible for better defect detection inside the materials inspected by frequency modulated thermal wave imaging. Novel constraints are formulated for autoencoder cost function to incorporate these significant properties. The proposed approach is able to provide better defect detection, in terms of signal to noise ratio of defects, than linear autoencoder as well as traditional principal component thermography approach. Also, non-correlation of encoded data is found to be the most significant factor in achieving better defect detection followed by properties ensuring weight orthogonality and weights with unit norm length.

Surface and Subsurface Eddy-Current Imaging With GMR Sensor

The high sensitivity for low-frequency fields makes the tiny giant magnetoresistance (GMR) sensors suitable for high-resolution eddy-current (EC) imaging. In this work, we proposed a novel design of the miniature differential EC probe with a spin-valve GMR sensor for surface and subsurface EC imaging. The differential EC probe consists of a full-bridge GMR chip mounted vertically on the edge of a tiny printed circuit board to reduce the liftoff distance. The excitation signal is perpendicular to the sensing axis of the GMR chip, which makes it sensitive to the unbalance EC signal on the surface of a conductive sample. The miniature EC probe operated at frequencies from 5 to 65 kHz is sensitive to the hidden defects on a flat conductor. The spatial resolution and stability of the device are verified by taking the 2-D scan images over the copper film samples with hidden defects, showing that the spatial resolution is better than 2 mm for the flaw 0.5 mm below the top surface of a multilayer structure. The proposed device is useful in the detection of small surface and subsurface defects, such as cracks, metal loss, and other mechanical damages.

An Improved Feature Parameter Extraction Algorithm of Composite Detection Method Based on the Fusion Theory

An improved feature parameter extraction algorithm is proposed in this study to solve the problem of quantitative detection of subsurface defects. Firstly, the common feature parameters from the differential signal of pulsed eddy current and ultrasonic are extracted in time domain and frequency domain. Then, the dispersion model and ReliefF model are established to determine the weights of each parameter. Finally, the weights from the two different algorithms are fused by the D‐S evidence theory to determine feature parameters. Compared with the PCA feature parameter algorithm from the pulsed eddy current or ultrasonic, the experiment results show the feature parameters extracted by the algorithm proposed in this paper are more effective in quantitative detection of subsurface defects. It will lead to high accuracy in the subsurface defections.

Probability of defect detection in glass fibre reinforced polymers using pulse compression favourable frequency modulated thermal wave imaging

Infrared Non-Destructive Evaluation (IRNDE) is an emerging approach among the Non-Destructive Testing (NDT) techniques to evaluate sub-surface defects, due to its non-contact, whole field, fast and quantitative defect detection abilities. Among various recently proposed aperiodic Thermal Non-destructive Testing and Evaluation (TNDT&E) methods, pulse compression favourable thermal wave imaging approaches gained significant importance due to their improved defect detection capability in terms of sensitivity and resolution. The present work attempts to explore the applicability of pulse-compression favourable Frequency Modulated Thermal Wave Imaging (FMTWI) approach for testing and evaluation of Glass Fibre Reinforced Polymer (GFRP) sample by considering the Peak to Side Lobe Ratio (PSLR) as a figure of merit. Results clearly depict that in pulse compression favourable FMTWI exhibits higher probability for detection of sub-surface defects of higher aspect ratio, with the subtraction of background temporal temperature distribution of the test sample in comparison with the presence of background.

Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study

Active and passive thermography are two efficient techniques extensively used to measure heterogeneous thermal patterns leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex-, and sparse- non-negative matrix factorization (NMF) methods for detecting subsurface thermal patterns. These methods inherit the advantages of principal component thermography (PCT) and sparse PCT, whereas tackle negative bases in sparse PCT with non-negative constraints, and exhibit clustering property in processing data. The practicality and efficiency of these methods are demonstrated by the experimental results for subsurface defect detection in three specimens (for different depth and size defects) and preserving thermal heterogeneity for distinguishing breast abnormality in breast cancer screening dataset (accuracy of 74.1%, 75.8%, and 77.8%).

Fusion and Visualization of Bridge Deck Nondestructive Evaluation Data via Machine Learning

To maintain infrastructure safety and integrity, nondestructive evaluation (NDE) technologies are often used for detection of subsurface defects and for holistic condition assessment of structures. While the rapid advances in data collection and the diversity of available sensing technologies provide new opportunities, the ability to efficiently process data and combine heterogeneous data sources to make robust decisions remains a challenge. Heterogeneous NDE measurements often conflict with one another and methods to visualize integrated results are often developed ad hoc. In this work, we present a framework to support fusion of multiple NDE techniques in order to improve both detection and quantification accuracy while also improving the visualization of NDE results. For data sources with waveform representations, the discrete wavelet transform (DWT) is used to extract salient features and facilitate fusion with scalar-valued NDE measurements. The description of a signal in terms of its salient features using a wavelet transform allows for capturing the significance of the original data, while suppressing measurement noise. The complete set of measurements is then fused using nonparametric machine learning so as to relax the need for Bayesian assumptions regarding statistical distributions. A novel visualization schema based on classifier confidence intervals is then employed to support holistic visualization and decision making. To validate the capabilities of the proposed methodology, an experimental prototype system was created and tested from NDE measurements of laboratory-scale bridge decks at Turner-Fairbank highway research center (TFHRC). The laboratory decks exhibit various types of artificial defects and several non-destructive tests have already been carried out by research center technicians to characterize the mechanical properties of the materials and the existing damages. The behaviors of several different data fusion approaches are compared here. Based on the presented analyses, it is demonstrated that fusion via support vector machines provided the most robust and consistent data fusion and defect detection capabilities.

Semi real-time detection of subsurface consolidation defects during concrete curing stage

Subsurface consolidation defect is a common issue in concrete pavement construction, and the hidden defects often require costly repairs after project delivery. Therefore, being able to identify this type of defect during construction will enable contractor to conduct quick repairs to avoid costly post-construction rework. In this paper a semi real-time detection approach using infrared thermography is introduced. The developed approach utilizes the hydration heat during curing time to identify the subsurface voids based on the regional temperature contrast. Experimental studies were conducted using artificial void-defect in different sizes and depths. The thermographic analysis is employed to locate the void-defects during the first 12 h of curing time. Two field experiments were conducted to validate the proposed approach. The outcomes show that the defects could be identified and located within certain time windows during the curing process, even under adversary outdoor environmental conditions. In conclusion, infrared thermography can be used as a feasible and reliable non-destructive-detection method to conduct real-time or semi real-time monitoring of subsurface defects during concrete pavement construction. The influential environmental factors, practical considerations, and the future study are also discussed.

Multiview Learning for Subsurface Defect Detection in Composite Products: A Challenge on Thermographic Data Analysis

Nondestructive testing (NDT) is an economical way of detecting subsurface defects in composite products. Infrared thermography serves as a popular NDT method due to its high efficiency and low cost. However, defect identification by directly visualizing thermal images is difficult owing to the nonuniform background and noise. Recently, data analysis methods have been introduced to thermal image processing, including principal component analysis, which is known for its good performance in dimensionality reduction, feature extraction, and noise reduction. However, most of these methods can only extract linear features. In this article, a multiview learning-based autoencoder, which can process not only nonlinear features but also sequential attributes, is utilized in thermographic data analysis. After extracting the low-dimensional features by multiview learning, a background elimination step is conducted to highlight the locations and shapes of the defects. The experimental results demonstrate the feasibility of the proposed method.

Evaluation of subsurface defects in metallic structures using laser ultrasonic technique and genetic algorithm-back propagation neural network

An effective nondestructive evaluation technique that enables the detection and quantification of subsurface defects is highly demanded for assuring safety and reliability of safety-critical structures. In this work, an improved genetic algorithm-back propagation neural network (GA-BPNN) model and non-contact laser ultrasonic technique are combined to quantify the width of subsurface defects. An experimentally validated numerical model that simulates the interaction of laser-generated Rayleigh ultrasonic waves with subsurface defects is firstly established, which is further utilized to generate a large number of labeled laser ultrasonic signals for training the GA-BPNN model. A total number of 189 data are obtained from simulation and experiments, with 173 simulated signals for training the GA-BPNN model and the remaining 13 simulated signals together with 3 experimental signals for verifying the performance of the trained GA-BPNN model. Five features including three time-domain features (maximum, minimum and peak-to-peak value of the Rayleigh ultrasonic waves) and two frequency-domain features (Fc, BW-6dB), which are identified sensitive to the width of subsurface defects by both experiments and simulation, are extracted as inputs to train the machine learning algorithm. The result demonstrates that the GA-BPNN model trained with the combination of time and frequency features has the average error of 2.15%, which is substantially smaller than the errors obtained from the model trained with only time-domain features and frequency-domain features, with the average errors of 4.43% and 21.81%, respectively. This work proves the feasibility and reliability to quantify the width of subsurface defects in metallic structures using laser ultrasonic technique and the improved GA-BPNN algorithm.

Deep learning models for bridge deck evaluation using impact echo

Impact echo (IE) is a common nondestructive evaluation (NDE) method to detect subsurface defects in concrete bridge decks. The conventional approach for analyzing the IE data requires user expertise to define analysis parameters that could hinder broad field implementation. In this paper, the feasibility of using deep learning models (DLMs) for autonomous subsurface defect detection in bridge decks using IE has been investigated. A set of eight laboratory-made reinforced concrete bridge specimens with artificial defects were constructed at the Federal Highway Administration Advanced Sensing Technology NDE laboratory. A total number of 2016 of IE data was collected from these specimens. A one-dimensional (1D) convolutional neural network (CNN), and a 1D recurrent neural network using bidirectional long-short term memory units, were developed and applied on the IE data. In addition, two-dimensional (2D) world renowned CNNs were applied on the 2D representatives of the IE data, i.e., spectrograms. The proposed 1D CNN was the most accurate model achieving an overall accuracy of 0.88 by classifying 0.70 of the defects and 0.95 of the sound regions correctly. The proposed 1DCNN was superior to previous machine learning models that were previously used for IE classification. The results of this study showed the feasibility and the potentials of the DLMs for subsurface defect detection.

Application of common-path speckle interferometer with unlimited minimal shearing amount to characterization of irregularly shaped notch

Non-destructive evaluation (NDE) techniques are widely used to detect and characterize different defects in engineering structures. However, the highly sensitive real-time quantitative imaging of irregularly shaped defects in large-area metallic structures remains challenging if conventional NDE approaches are used. In this paper, a speckle interferometer with unlimited minimal shearing amount – based on a simple imaging setup without the need to tilt any optical components – is explored for the application of detection and characterization of complex-shaped defects in metallic plates. This technology is able to directly yield strain measurements, allowing the defects to be detected by identifying induced strain concentrations under stress loading. Acoustic waves are used as stress loading for this imaging system with the advantage of a deeper penetration depth and a larger area of inspection as compared to conventional loading methods, e.g. thermal loading. The spatial carrier technique is introduced to quantitatively reconstruct the phase distribution, which contains information of the strain measurement from a single recording. To achieve the near real-time imaging of defects with relative insensitivity against noise, the image pairs are continuously recorded and correlated using the sequential subtraction method. The performance of the imaging system was tested with experimental data. The results demonstrate that the imaging system can generate high-quality images to provide good detection and characterization properties regarding irregularly shaped notches as well as accurate sizing of different notch dimensions. This work introduces a promising practical NDE tool for the near real-time, non-contacting and quantitative detection of subsurface defects with complex geometries in large-area metallic structures.

Subsurface defect detection using phase evolution of line laser-generated Rayleigh waves

An unreported phenomenon of phase evolution of Rayleigh ultrasonic waves with subsurface defects is observed and systematically explored for detection of subsurface defects using non-contact line laser ultrasonic technique and numerical simulation. The mechanism of phase evolution of Rayleigh wave signals is explained by the interference of the reflected and direct Rayleigh wave, explored by finite element analysis. Both experiments and simulation show distinct peak evolution of the Rayleigh wave signals with the width and depth of subsurface defect. A dimensionless parameter (|Neg|/Pos), defined by the ratio of absolute negative peak to positive peak of Rayleigh wave, is proposed to evaluate the phase evolution of Rayleigh wave with defect width and depth, which is further used to quantify the subsurface defects. The phase evolution of Rayleigh waves can act as a robust and sensitive feature to detect subsurface defects using laser-generated ultrasound, which has promising applications in life prediction and health monitoring of various engineering structures.