Current Inspection Techniques Research Articles

Ultrasonic metrics for large-area rapid wrinkle detection and classification in composites

Due to their high strength-to-weight ratio, composite materials are now in use in many high-stress applications, particularly where light weight is also a requirement. In these situations, the detrimental knock-down in mechanical strength due to an out-of-plane wrinkle defect can have serious consequences and is the reason for a requirement to rapidly detect any such wrinkles at manufacture. Unfortunately, current ultrasonic inspection techniques used for quality control at manufacture are not sensitive enough to detect these wrinkles above coherent structural noise variations. This paper exploits the ply resonance that is a characteristic of multi-layer structures to generate two new metrics for both detection and classification of out-of-plane wrinkles, due to their perturbations of the ply spacing. These can be measured at every location on a structure using the instantaneous frequency, which is the rate of change of phase in the pulse-echo ultrasonic response. The proposed two new metrics for detection and classification of wrinkles are mean spacing and spacing difference and they can be applied to each waveform in real time, as it is acquired. Use of an analytical model to predict the ultrasonic response of the structure has allowed an understanding of how these metrics will be affected by various wrinkle types and how they can not only detect wrinkles but also classify the type of wrinkle and provide an approximate indication of severity. Three main types of wrinkle are considered: classic wrinkles near the mid-plane of a structure, back-surface wrinkles formed from a resin bulge near the back of a structure and folded wrinkles where several plies can be folded over completely in the bulk of the structure. Both simulations and experimental results demonstrate the effectiveness of these metrics on various types of structure, including carbon-fibre and hybrid carbon/glass-fibre composites with a range of ply thicknesses and wrinkle types.

Non-destructive corrosion inspection of reinforced concrete structures using an autonomous flying robot

Autonomous non-destructive testing (NDT) on reinforced concrete structures has a large potential to overcome the limitations of current routine inspection techniques, often not capable of detecting corrosion at an early stage. Here, the development and validation of two probes, tailored to acquire contact-based NDT data with the help of a hexacopter, is presented. Each probe allows the combined measurement of two essential parameters in the condition assessment: half-cell potentials and concrete electrical resistivity. Strategies are presented to monitor probe functionality during operation and detect loss of contact between probe and structure, which is considered essential for autonomous NDT. The presented approach enables effective and reliable autonomous corrosion inspection, surpassing traditional visual inspections by localizing corrosion at an early stage, allowing engineers a better planning of maintenance. The successful application in a concrete bridge inspection sets the stage for future research in autonomous inspections with robots in various fields of applications.

Blending hydrogen in existing natural gas pipelines: Integrity consequences from a fitness for service perspective

Blending hydrogen in existing natural gas pipelines compromises steel integrity because it increases fatigue crack growth, promotes subcritical cracking and decreases fracture toughness. In this regard, several laboratories reported that the fracture toughness measured in a hydrogen containing gaseous atmosphere, KIH, can be 50% or less than KIC, the fracture toughness measured in air. From a pipeline integrity perspective, fracture mechanics predicts that injecting hydrogen in a natural gas pipeline decreases the failure pressure and the size of the critical flaw at a given pressure level. For a pipeline with a given flaw size, as shown in this work, the effect of Hydrogen Embrittlement (HE) in the predicted failure pressure is largest when a failure occurs by a brittle fracture. The HE effect on failure pressure diminishes with a decreasing crack size or increasing fracture toughness. The safety margin after a successful hydrostatic test is reduced and therefore the time between hydrotests should be decreased. In this work, all those effects were quantified using a crack assessment methodology (level 2, API 579-ASME FFS) considering literature values for KIH and KIC reported for an API 5L X52 pipeline steel. To characterize different scenarios, various crack sizes were assumed, including a small crack with a size close to the detection limit of current in-line inspection techniques and a larger crack that represents the largest crack size that could survive a hydrotest to 100% of the steel Specified Minimum Yield Strength (SMYS). The implications of a smaller failure pressure and smaller critical crack size on pipeline integrity are discussed in this paper.

Weather-dependent passive thermography and thermal simulation of in-service wind turbine blades

To cope with the increase in the manufacturing and operation of wind turbines, wind farm operators need inspection tools that are able to provide reliable information while keeping the downtime low. Current inspection techniques require the wind turbine to be stopped. This work presents the current progress in the project EvalTherm, in which passive thermography is evaluated as a possible non-destructive inspection tool for operational wind turbine blades (WTBs). A methodology to obtain thermal images of rotating WTBs has been established in this project. However, the quality of the results is heavily dependent on various aspects such as weather conditions, information on the inspected WTB, damage history, etc. In this work, a section of a used WTB is simulated using finite-element modelling (FEM) as well as experimentally tested for evaluating the accuracy of the model. Such a model will provide insight into the potential thermal response of a certain structure (with specific material properties) in given weather (boundary) conditions. The model is able to provide satisfactory predictions of the temporal thermal response of the structure, as well as indicate what thermal contrast(s) transients result from artificial defects introduced in the structure.

Strip steel surface defect detecting method combined with a multi-layer attention mechanism network

In the production of strip steel, defect detection is a crucial step. However, current inspection techniques frequently suffer from issues like low detection accuracy and subpar real-time performance. We provide a deep learning-based strip steel surface defect detection technique to address the aforementioned issues. The algorithm is also implemented in three specific ways: as the backbone, the neck, and the detection head. Backbone employs an enhanced cross stage partial in conjunction with ResNet to effectively mine defect information and minimize the issue of adjoining feature maps’ neighboring feature maps losing information. Neck is a better structure, we propose and use the dilated weighted across stages-feature pyramid network in the network to adjust the receptive field and attention weight preference of the output feature maps at different scales and to improve the utilization of defect features by the algorithm to enhance the detection of abnormal size defects. We use four detection heads in the detection head so that the network can learn the features of defects of various sizes. Finally, we use the decoupled head to separate the classification work from the regression work before combining the prediction. Two datasets of surface flaws in strip steel are used in our experiments (GC10-DET and NEU-DET). In addition, it has been shown that our proposed algorithm’s mAP in GC10-DET and NEU-DET reaches 79.93% and 72.76%, respectively, resulting in a better detection impact.

Wavelets and curvelets transform for image denoising to damage identification of thin plate

As a common structural form, thin plates are widely used in civil engineering. Since the thin plate needs to face harsh work conditions, the damage inevitably to be accumulated, thus affecting the stability and safety of the application components. Therefore, it is of great application significance to quantify and characterize the damage of thin plates. However, the raw images produced by current inspection techniques such as Ultrasonic immersion C-scan technology, Metal sheet Lamb wave inspection technology, etc applied to thin plates usually bring various noises and imperfections during the reception, encoding, and transmission. In this paper, wavelet transform and Curvelet transform are used to denoise the detected noise image. First, we outline the numerical implementation of two newly developed multi-scale representation systems. Curvelet transform is a new multi-scale transform based on wavelet transform after 1999. The purpose of this paper is to analyze the influence of wavelet and Curvelet transform on image denoising. These methods can also be applied to the problem of image restoration from noisy images, and the effects of denoising on images are compared. The results show that the Curvelet transform can accurately identify the damage location for the thin plate damage degree, damage range, strip damage, and multiple damage conditions, and its energy focusing is better than that of the wavelet transform in each type of thin plate damage.

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. In this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

Aerial Robotic Solution for Detailed Inspection of Viaducts

The inspection of public infrastructure, such as viaducts and bridges, is crucial for their proper maintenance given the heavy use of many of them. Current inspection techniques are very costly and manual, requiring highly qualified personnel and involving many risks. This article presents a novel solution for the detailed inspection of viaducts using aerial robotic platforms. The system provides a highly automated visual inspection platform that does not rely on GPS and could even fly underneath the infrastructure. Unlike commercially available solutions, our system automatically references the inspection to a global coordinate system usable throughout the lifespan of the infrastructure. In addition, the system includes another aerial platform with a robotic arm to make contact inspections of detected defects, thus providing information that cannot be obtained only with images. Both aerial robotic platforms feature flexibility in the choice of camera or contact measurement sensors as the situation requires. The system was validated by performing inspection flights on real viaducts.

Natural Rail Surface Defect Inspection and Analysis Using 16-Channel Eddy Current System

Trains are used as the fastest mode of transportation for both people and cargo. The train moves along a special path called “rail”, where fatigue can be accumulated due to wheel-rail contact load as a result of continuous train operation. Consistent and regularly scheduled safety management is required since corrosion rate of the rails located on outside environment is very high. Researchers have actively investigated and developed rail defect inspection systems employing non-destructive techniques to address these problems. In particular, the eddy current inspection technique does not involve contact with the surface of the test specimen and offers the advantage of excellent rail defect detection sensitivity. Therefore, a 16 Channel array eddy current inspection device was developed to inspect the surface defects of the rail. An equation was derived to predict the correlation between the depth and phase of an artificial defect using the eddy current inspection device, and the derived equation was applied to the natural defect specimen.

Numerical Modelling of Ultrasonic Guided Wave Propagation and Defect Detection in Offshore Steel Sheet Piles

Sheet piles are significantly more prone to advanced corrosion rates due to accelerated low water corrosion. Current inspection and assessment techniques are costly, time-consuming and labour-intensive. Guided wave testing (GWT) has gained increased attention due to its capability of screening long distances; however, it has not been used previously to inspect the active zone in steel sheet piles. This paper focuses on the numerical modelling of wave propagation and defect detection in U-shaped piles to demonstrate the capabilities of GWT for the inspection of non-accessible areas of steel sheet piles. Two shear transducer arrays were designed, bearing high SH0 mode purity and directionality. A wave propagation comparison study concluded that the back wall reflection signal from the web of a U-pile was 11.5% higher than the respective signal from the plate, and the excitation signal in the flange, at 5.65 m and 7.12 m, was respectively 35% and 46% less than the excitation signal in the web at the same distance. Defect reflection, measured from five representative defect scenarios, ranged from 7.5 to 47% of the signal amplitude in the web of the pile and 5 to 32.5% in the flange of the pile.

Automated classification of civil structure defects based on convolutional neural network

Today, the most commonly used civil infrastructure inspection method is based on a visual assessment conducted by certified inspectors following prescribed protocols. However, the increase in aggressive environmental and load conditions, coupled with the achievement of many structures of the life-cycle end, has highlighted the need to automate damage identification and satisfy the number of structures that need to be inspected. To overcome this challenge, this paper presents a method for automating concrete damage classification using a deep convolutional neural network. The convolutional neural network was designed after an experimental investigation of a wide number of pretrained networks, applying the transfer-learning technique. Training and validation were conducted using a database built with 1352 images balanced between “undamaged”, “cracked”, and “delaminated” concrete surfaces. To increase the network robustness compared to images in real-world situations, different image configurations have been collected from the Internet and on-field bridge inspections. The GoogLeNet model, with the highest validation accuracy of approximately 94%, was selected as the most suitable network for concrete damage classification. The results confirm that the proposed model can correctly classify images from real concrete surfaces of bridges, tunnels, and pavement, resulting in an effective alternative to the current visual inspection techniques.

Feature recognition and strength estimation of chain links by 3D inspections

In the past mooring chain failure probabilities have been much higher than the design failure probabilities. The high failure rates are attributed to prolonged exposure to various degradation mechanisms in harsh offshore conditions. In addition to understanding failure mechanisms, researchers have stressed on reducing the risk of failures through regular and reliable inspections. Current inspection techniques involve both in-air and in-water inspections. However, current in-air inspections are time consuming and expensive, while in-water inspection techniques are not yet reliable. The present paper proposes a condition monitoring and strength estimation methodology based on three-dimensional (3D) scans of links. The study first identifies and estimates changes in simple geometrical features of chain links using finite element (FE) modeling for condition monitoring. The methodology has two stages: 3D general imaging (3D-GI) to identify degraded chain links using feature monitoring, followed by 3D detailed imaging (3D-DI) of the identified weak links. The weak links 3D point clouds could be further converted to solid FE models for residual strength estimation by employing a suitable material degradation model. The geometrical feature monitoring methodology is verified by 3D scans of an undeformed and a deformed studded link.

Maximal overlap discrete wavelet-packet transform aided microwave nondestructive testing

The capability of microwave signals to penetrate inside composites and interact with the inner structure makes them a very attractive candidate for composite inspection. Various techniques of microwave nondestructive testing (NDT) are used for detecting disbonds in composites. Despite their promising results, these techniques suffer from poor spatial resolution due to the given features that do not significantly distinguish between the defect and defect-free regions. In this paper, a hybrid signal processing based on a refinement feature extraction method is employed to enhance the imaging efficiency of the disbond detection in composite material. This technique is based on scanning the composite material with an open-ended rectangular waveguide operating from 18 to 26.5 GHz and analyzing its reflections using the proposed hybrid signal processing method. Maximal overlap discrete wavelet packet transform is employed to provide significant informative features of each frequency point. The Bi-directional long short-term memory (Bi-LSTM) network approach is used to distinguish the significant features and the outliers. The Bi-LSTM classifies each inspected location into a defect or defect-free location. The findings presented in this paper show the advantages of the refinement method-aided microwave technique in detecting disbonds down to 1 mm with an accuracy rate of 88.84%, a significant advantage over any current disbond inspection technique.

Low-cost contactless monitoring of aseptic package integrity and content quality using capacitance

Aseptic packaging is employed by food and beverage industries to maintain sterility for prolonging product shelf life without refrigeration. In a fully integrated fast-moving consumer goods (FMCG) production line, high throughput non-destructive detection of visually non-obvious packaging defects like pinholes is a challenge using current inspection techniques. A non-contact capacitive electronic sensing platform based on the tuneable sensing depth under an applied sinusoidal signal is proposed for monitoring the changes in capacitance due to the effect of pinholes on the package integrity (package dimensional changes) and content quality (dielectric changes). The real-time capacitive system successfully detects pinhole formation with good detection speed (< 3 s) and low detection limit (∼54.69 μm), identifies the onset of product decay and captures subsequent package deformity using a combination of sensing topologies and location information. The demonstrated multi-phase detection proof-of-concept could translate into contactless aseptic package/product quality monitoring systems for production, storage and retail.

Augmented reality for next generation infrastructure inspections

This article introduces the use of emerging augmented reality technology to enable the next generation of structural infrastructure inspection and awareness. This work is driven by the prevalence of visual structural inspection. It is known that current visual inspection techniques have multiple sources of variance that should be reduced in order to achieve less ambiguous visual inspections. Emerging augmented reality tools feature a variety of sensors, computation, and communication resources that can enable relevant structural inspection data to be collected at very high resolution in an unambiguous manner. This work shows how emerging augmented reality tools can be used to greatly enhance our ability to capture comprehensive, high-resolution, three-dimensional measurements of critical infrastructure. This work also provides detailed information on the software architecture for augmented reality structural inspection applications that helps meet the goals of the framework. The fact that the framework is designed to accommodate the considerations associated with high-consequence infrastructure implies that it is also comprehensive enough to be applied to less hazardous but still high-value infrastructure such as bridges, dams, and tunnels. Augmented reality has great potential to enable the next generation of smart infrastructure, and this work focuses on addressing how augmented reality can be leveraged to enable the next generation of structural awareness for high-consequence, long-lifespan structures.

A Novel Inspection Technique for Electronic Components Using Thermography (NITECT).

Unverified or counterfeited electronic components pose a big threat globally because they could lead to malfunction of safety-critical systems and reduced reliability of high-hazard assets. The current inspection techniques are either expensive or slow, which becomes the bottleneck of large volume inspection. As a complement of the existing inspection capabilities, a pulsed thermography-based screening technique is proposed in this paper using a digital twin methodology. A FEM-based simulation unit is initially developed to simulate the internal structure of electronic components with deviations of multiple physical properties, informed by X-ray data, along with its thermal behaviour under exposure to instantaneous heat. A dedicated physical inspection unit is then integrated to verify the simulation unit and further improve the simulation by taking account of various uncertainties caused by equipment and samples. Principle component analysis is used for feature extraction, and then a set of machine learning-based classifiers are employed for quantitative classification. Evaluation results of 17 chips from different sources successfully demonstrate the effectiveness of the proposed technique.

Excitation of Single-Mode Shear-Horizontal Guided Waves and Evaluation of Their Sensitivity to Very Shallow Crack-Like Defects

Inspection is a key part of the asset management process of industrial plants and there are numerous plate-like structures that require inspection. Ultrasonic guided waves have been extensively used to detect various types of defect by monitoring reflected and transmitted signals because they enable faster screening of large areas. However, ultrasonic guided wave testing becomes difficult for very shallow, sharp defects as current inspection techniques suffer from a lack of sensitivity to such features. Previous studies, obtained by comparing various inspection techniques, suggest that the SH1 mode in particular, at around 3 MHz · mm, would be suitable when testing for shallow defects; however, it is clear that both the SH0 and SH1 modes can exist at this frequency-thickness product. This can complicate the inspection process and, therefore, limit defect detectability. This article investigates the possibility of a single-mode excitation of the SH1 mode at around 3 MHz · mm. The ability of this method toward detecting very shallow defects (<10% cross-sectional thickness loss) has also been studied. By means of analytical predictions and finite element, it is shown that a signal dominated by the SH1 mode can be generated using a single permanent periodic magnet (PPM) electromagnetic acoustic transducer (EMAT) (PPM EMAT). All predictions are then backed up by experimental measurements. It is also shown that, by studying the reflection coefficient of the SH1 mode, the pure SH1 mode can be used to detect defects as shallow as 5% thickness loss from a 500-mm stand-off. These defects would otherwise be missed by standard, lower frequency guided wave testing.

Detection of the Fatigue Cracks Initiated near the Rivet Holes by Eddy Current Inspection Techniques

Abstract Eddy current (EC) method is considered as most applicable for in-service detection of fatigue subsurface cracks initiated in aircraft multilayer structures near the rivet holes. At the same time, the successful solution of this problem is obstructed by additional noise created by defect-free rivets. All EC inspection techniques for the detection of subsurface cracks around the rivets can be classified into three main groups: 1) static mode – carried out by placing the EC probe concentrically on the rivet head; 2) rotational mode – when the EC probe is rotated around the rivet axle and 2) sliding mode – performed by the movement of EC probe along the rivet line or near it. All these approaches have some advantages and limitations. In this study, known EC techniques for the detection of cracks in multilayer aircraft structures are analyzed. New advanced EC techniques for the detection of fatigue cracks in internal layers of the riveted structures based on different types (ring, sliding, and rotational) probes are presented. The static EC method with developed low-height ring-type probe creates the possibility to detect cracks in the difficult of access areas. The possibility to estimate the length of detected cracks by a ring-type probe is shown. The proposed rotational remote field EC probe can detect as small as 1.0 mm long cracks under the button-head rivet and 2 mm thick upper skin with a high signal-to-noise ratio. Therefore, in many aircraft structures, fatigue cracks will be detected before a critical threshold achieved. New EC sliding techniques based on remote field and double differential probes were proposed for the rapid detection of cracks in internal layers of riveted aircraft structures. Remote-field EC probe for reliable detection of fatigue cracks in third and fourth layers of five-layer units was proposed. Another sliding technique based on a double differential EC probe gives the possibility to detect transverse cracks in the second layer without the rivet row area access. The main advantage of developed techniques is high inspection reliability due to the possibility to discriminate the signals created by cracks and defect-free rivets. Presented inspection procedures include the selective signal analysis in the complex plane diagram. Proposed EC inspection techniques were successfully implemented into the aircraft maintenance practice.

Leakage detection in water pipelines using supervised classification of acceleration signals

Loss of treated water through buried pipeline leakage is one of the pressing challenges faced by water utilities across the world. Many current pipeline inspection techniques are ad-hoc in nature and only provide a snapshot status of the system. Although a few embedded sensor-based systems offer promise for continuous monitoring, they are limited in their ability to detect leakages of all magnitudes. Lack of a comprehensive understanding of the pipeline system's structural dynamics and the correlation to leakage detection is a primary challenge. To overcome this challenge, a deep learning algorithm that uses scalogram images of vibration signals collected from accelerometers attached to the pipeline surface is developed and validated in this study. The advantage is that the complex structural features of the water pipeline systems need not be modeled if sufficient leak-based vibration signal data is available for training purposes. A convolutional neural network (CNN) model adapted from a pre-trained AlexNet network is leveraged to demonstrate the proposed approach using vibration data collected from an experimental pipeline test bed. The CNN model is found to be able to detect leakages on polyvinyl chloride (PVC) pipelines with up to 95% accuracy. A maximum accuracy of 98% is observed with acceleration signal data collected from carefully selected locations. The approach presented in this study can be adapted for effective leakage detection in real world scenarios with minimum human intervention.

Optimization of digital radiography for large metallic additively manufactured components

PurposeThe continued improvement of additive manufacturing (AM) processing has led to increased part complexity and scale. Processes such as electron beam directed energy deposition (DED) are able to produce metal AM parts several meters in scale. These structures pose a challenge for current inspection techniques because of their large size and thickness. Typically, X-ray computed tomography is used to inspect AM components, but low source energies and small inspection volumes restrict the size of components that can be inspected. This paper aims to develop digital radiography (DR) as a method for inspecting multi-meter-sized AM components and a tool that optimizes the DR inspection process.Design/methodology/approachThis tool, SMART DR, provides optimal orientations and the probability of detection for flaw sizes of interest. This information enables design changes to be made prior to manufacturing that improve the inspectabitity of the component and areas of interest.FindingsValidation of SMART DR was performed using a 40-mm-thick stainless-steel blade produced by laser-based DED. An optimal orientation was automatically determined to allow radiographic inspection of a thickness of 40 mm with a 70% probability of detecting 0.5 mm diameter flaws. Radiography of the blade using the optimal orientation defined by SMART DR resulted in 0.5-mm diameter pores being detected and indicated good agreement between SMART DR’s predictions and the physical results.Originality/valueThis paper addresses the need for non-destructive inspection techniques specifically developed for AM components.