Reliable Inspection Research Articles

Monitoring of bolt loosening using vibro-acoustic modulation (VAM)

Detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave-surface interactions at the joint interfaces. Such coefficients show a poor correlation with the tightening torque when the joint is under the combined influences from structural and material nonlinearities. A reliable inspection method of residual bolt torque was proposed in this study using Support Vector Regression (SVR) with acoustic features from VAM. By considering material intrinsic nonlinearity (MIN) and dissipative nonlinearity (DN), responses of aluminum-aluminum and composite-composite bolted joints during the VAM test were accurately simulated. SVR were subsequently established based on the database built by a mixing of simulated and experimental nonlinear spectral features when the joints were inspected at different scenarios. The results show that the evaluation of residual torque using the SVR models driven by the acoustic nonlinear responses show a higher accuracy compared with the conventional nonlinear coefficients. Requiring limited experiment data, the proposed method can achieve reliable inspection of bolt torque by including the simulated data into machine training.

Complementary Methods for the Assessment of the Porosity of Laser Additive-Manufactured Titanium Alloy.

The method of making parts through additive manufacturing (AM) is becoming more and more widespread due to the possibility of the direct manufacturing of components with complex geometries. However, the technology's capacity is limited by the appearance of micro-cracks/discontinuities during the layer-by-layer thermal process. The ultrasonic (US) method is often applied to detect and estimate the location and size of discontinuities in the metallic parts obtained by AM as well as to identify local deterioration in structures. The Ti6Al4V (Ti64) alloy prepared by AM needed to acquire a high-quality densification if remarkable mechanical properties were to be pursued. Ultrasonic instruments employ a different type of scanning for the studied samples, resulting in extremely detailed images comparable to X-rays. Automated non-destructive testing with special algorithms is widely used in the industry today. In general, this means that there is a trend towards automation and data sharing in various technological and production sectors, including the use of intelligent systems at the initial stage of production that can exclude defective construction materials, prevent the spread of defective products, and identify the causes of certain instances of damage. Placing the non-destructive testing on a completely new basis will create the possibility for a broader analysis of the primary data and thus will contribute to the improvement of both inspection reliability and consistency of the results. The paper aims to present the C-scan method, using ultrasonic images in amplitude or time-of-flight to emphasize discontinuities of Ti64 samples realized by laser powder-bed fusion (L-PBF) technology. The analysis of US maps offers the possibility of information correlation, mainly as to flaws in certain areas, as well as distribution of a specific flaw in the volume of the sample (flaws and pores). Final users can import C-scan results as ASCII files for further processing and comparison with other methods of analysis (e.g., non-linear elastic wave spectroscopy (NEWS), multi-frequency eddy current, and computer tomography), leading to specific results. The precision of the flight time measurement ensures the possibility of estimating the types of discontinuities, including volumetric ones, offering immediate results of the inspection. In situ monitoring allows the detection, characterization, and prediction of defects, which is suitable for robotics. Detailing the level of discontinuities at a certain location is extremely valuable for making maintenance and management decisions.

REVIEW ON NON-DESTRUCTIVE TECHNIQUES FOR EARLY FLAW DETECTION IN INSPECTIONS

Non-destructive testing (NDT) is critical in ensuring various materials’ and structures’ integrity and safety by detecting flaws without causing damage. Early flaw detection is essential for preventing catastrophic failures, reducing maintenance costs, and enhancing operational efficiency. This research paper comprehensively reviews non-destructive techniques for early flaw detection in inspections. The paper begins with an introduction to the significance of flaw detection and the motivation behind employing non-destructive techniques. It proceeds with a thorough literature review, summarizing existing research and highlighting the strengths and limitations of different methods. The main section of the paper delves into the description and principles of common non-destructive techniques, including Ultrasonic Testing (UT), Radiographic Testing (RT), Magnetic Particle Testing (MPT), Liquid Penetrant Testing (LPT), Eddy Current Testing (ECT), Thermography, Acoustic Emission Testing (AET), and others. The efficacy of each technique is explored through various case studies and real-world applications across diverse industries. Advantages, limitations, and factors influencing technique selection in specific scenarios are critically analyzed. Furthermore, the paper discusses recent advancements and emerging technologies in non-destructive testing that offer potential improvements for early flaw detection. It also covers relevant international and industry-specific standards and regulations governing NDT, emphasizing their significance in enhancing inspection reliability. The conclusion reaffirms the importance of non-destructive techniques for early flaw detection. It proposes future research directions to overcome existing challenges. This review aims to provide engineers, researchers, and practitioners with valuable insights into the state-of-the-art NDT methods, fostering continuous improvement in early flaw detection and ensuring the safety and reliability of critical infrastructures and materials.

Pollution prevention of vessels in the greater bay area: A practical contribution of port state control inspection system towards carbon neutralisation using a tree augmented naive bayes approach

Vessel emissions are one of the major emissions sources affecting carbon neutralisation achievement in the Greater Bay Area (GBA). The vessel pollution prevention ability is effective in preventing vessels from discharging mass emissions. Thus, it is of great value to figure out the vessel's performance in pollution prevention in this area. This study develops a data-driven Bayesian network model focusing on vessel performance in pollution prevention in port state control (PSC) inspections in the GBA. The Tree Augmented Naïve and Expectation Maximisation approaches are applied to learn structures and parameters based on the inspection records obtained from January 2015 to September 2022. An analysis can identify key variables with significant effects on vessel performance in pollution prevention and clarify the positive influence and contributions of PSC inspections toward carbon neutralisation achievement. Practical implications are provided to foster port authorities and local governments in the GBA to minimize the vessels with poor pollution prevention performance sailing in this area. The study also identifies the characteristics of vessels with poor performance and proposes valuable suggestions to restrict the inattentive actions of ship owners and stimulate ship owners to put more effort into the improvement of pollution prevention ability. Further, the study reveals the significance of executing efficient and reliable PSC inspections in promoting carbon neutralisation progress.

Development and testing of coarse-grained models for ultrasonic simulations of cast austenitic stainless steel

Ultrasonic inspection of cast austenitic stainless steel (CASS) in the nuclear industry is particularly challenging because of sound field scatter and attenuation caused by the coarse-grained microstructure. Modeling and simulation are important tools in ultrasonic testing, as they can be used to help address key aspects of inspections, such as developing new probe designs, predicting inspection reliability, and testing phased-array focal laws. However, developing a useful and reliable CASS model is challenging due to the many grain interfaces and crystalline orientations that must be captured. We demonstrate a method of creating a realistic CASS model that is usable in CIVA, a commercially available modeling and simulation software platform. Using polished and chemically etched sections to highlight the grain structure and orientation, we generate models of a coarse-grained equiaxed specimen and a columnar specimen. We also test an alternative method of generating a coarse-grained model using Voronoi regions. We qualitatively compare sound field scatter and quantitatively compare sound field attenuation and beam partitioning in simulated sound fields to those of laboratory-measured sound fields. Results show that the Voronoi models perform as well as or better than the models based on actual grain morphology. We also show that model-to-model randomness in Voronoi grain structure can impact the magnitude of a simulated echo response by a factor of two or more. Although CASS models are potentially a good depiction of reality for a given scenario, they should not be considered representative since CASS morphology can change significantly from specimen to specimen or within the same specimen.

Using Modeling and Metamodels for Reliability Study in Non-Destructive Evaluation

As in any other measurement process, NDE is subject to variability whose impact can be assessed to guarantee a given level of performance. Once NDE prevents catastrophic failures, deaths and environmental damage, identifying uncertainties and variability in NDE help to design more reliable inspections, therefore is a process that saves lives. This is the goal of a reliability study. Statistical indicators such as Probability of Detection (POD) curves give insights to allow building of mechanical designs with enough « secure margin » for structural integrity and to also define appropriate maintenance & inspection cycles. Simulation is very useful to support performance or reliability demonstrations that require a lot of data (such as POD studies and qualification campaigns), and where simulation can help by reducing the number of necessary mock-ups and experimental trials. In addition to physical models, the NDE simulation software CIVA now offers meta-modelling techniques. Built from an initial set of physical simulations, such surrogate models give the user the possibility to generate a massive amount of data while combining and exploring multi parametric variations. This is particularly efficient in the context of reliability studies, when you have to find the best settings, track the worst-case scenario or build POD curves. This paper illustrates the use of this meta-modelling approach for the reliability study of a longitudinal weld AUT inspection. Real pipe mill inspection data are provided and compared to modelling and meta-modelling results.

Mixed Reality procedures for the maintenance of existing bridges and retaining walls

AbstractNational and International road networks are made of structures and infrastructures that often exhibit poor performance due to a lack of maintenance. In this context, Italy recently renewed the legislation regarding existing infrastructure analysis to support knowledge and facilitate a homogeneous data acquisition process. Thus a key component of the process consists of the visual inspection protocols complemented by physical and mechanical measures. This applies to all the structures built for the road network operation, including those classified as geotechnical whose deterioration is often neglected or appropriately surveyed. Along with the definition of a data form for collecting datasets on these aspects, a relevant aspect of the process is the implementation of a collaborative environment for the information management and analysis systems supporting the interested parties in the field. The paper reports the design of a base geotechnical inspection form for Retaining Wall and illustrates some aspects of its implementation in a digital environment. Mixed Reality techniques in combination with web‐based tools for data collection are investigated to increase the feasibility and reliability of the visual inspection of existing road networks.

Corrosion damage detection in reinforced concrete using Rayleigh wave-based method

Corrosion of reinforcing bars is a dominant cause of degradation of reinforced concrete (RC) structures. Over time, expansive products of corrosion build up tensile stresses within the concrete, leading to cracks on the exterior surface and spalling of concrete cover. To extend the service life of RC structures, a reliable non-destructive inspection method is required to detect and evaluate corrosion-induced internal cracks in concrete before spalling, so that timely repair can be conducted. Ultrasonic Rayleigh waves have been employed to detect near-surface cracks in concrete based on their transmission coefficient in frequency domain or their time-of-flight information. However, due to heterogeneity of concrete materials, the ill-defined Rayleigh wave component and the sensor coupling on the rough surface of concrete have a significant influence on accuracy of the method. In this study, an improved method using Rayleigh waves has been investigated to detect corrosion-induced internal cracks in concrete. In the method, energy spectrum of the Rayleigh wave is analysed using the continuous wavelet transform and correlated to locations of internal cracks. This method is not sensitive to surface roughness of concrete members and does not need accurate identification of the Rayleigh wave components. Performance of the method was examined using corroded RC specimens with different cover depths and aggregate sizes. As a comparison, electrochemical tests were performed to map the corrosion current density. The test results showed very good agreement with the corrosion damage map generated from the proposed Rayleigh wave-based method.

Flaw Detection in Wire and Arc Additive Manufacturing Using In-Situ Wide Frequency Bandwidth Acoustic Pressure

Wire and arc additive manufacturing (WAAM) is an additive manufacturing (AM) process that can produce large metallic components with low material waste and high production rates. However, WAAM’s high deposition rates require high heat input which can result in potential defects such as pores, cracks, lack of fusion or distortion. For practical implementation of the WAAM process in an industrial environment it is necessary to ensure defects-free production. However, the NDT inspection using traditional NDT techniques (ultrasound, eddy currents, x-ray, for example) is a very demanding task, especially during part production. Therefore, reliable online NDT inspection and monitoring techniques are needed for the industrial spread of WAAM. The objective of this work is to detect flaw formation on WAAM produced parts using in-situ acquired acoustic data with a frequency bandwidth from 10 to 1MHz. WAAM parts were processed with deliberately introduced contaminations while its acoustic signal was obtained to correlate different signal characteristics with defects. To identify flaw formation, two distinct types of microphones were employed to acquire data from the same deposition process. The processing of the signal consisted of applying time and frequency domain techniques, namely, Power Spectral Density and Short Time Fourier Transform. The acoustic signatures obtained allowed for the differentiation between flawed and flaw free signals and for the spatial location of the contaminations. The acoustic signal acquired also showed that the data acquired by conventional microphones is not enough to fully characterize the acoustic spectrum emitted by the WAAM process. This work demonstrates the potential of acoustic data and signal processing in the online inspection of WAAM produced parts.

Total Focusing (TFM) for the Ultrasonic Testing (UT) of drawn arc stud welding

This article describes the results obtained applying ultrasonic testing (UT) with phased arrays (PAUT) using full matrix capture (FMC) and total focusing method (TFM) for the inspection of drawn arc stud welding. Weld studs are usually directly applied on the top of a metal slab using a dedicated welding gun. This procedure involves heating the end tip of the stud using a strong electric arc created by a positive electric current. This welding procedure shall comply to various normative requirements, but on the other side there is not a clear reference to any applicable non-destructive method (NDT) to control the quality of the weld seam. Shear connectors are normally used in concrete/steel composite structures to allow the combined action of the concrete slab and the steel profile thereby increasing both the stiffness and strength of the structure. Flaws in this connection would result in severe bending and cracking of the entire structure. This article proposes an advanced UT method to inspect the weld between shear connections and metal slab using FMC. Data acquired with FMC are processed by TFM algorithms improving image resolution, signal to noise performances and allowing 3D data imaging. The combined use of PAUT searching units and signal processing with high resolution measurement shows a great potential in industrial NDT applied to civil engineering, resulting in a more effective, reliable, and faster inspection methodology than many other methods, such as radiographic testing (RT).

A novel image registration-based dynamic photometric stereo method for online defect detection in aluminum alloy castings

The adoption of three-dimensional (3D) measurement technology for parts surface defect detection can improve inspection reliability. For online inspection purposes, 3D measurement technologies must possess the characteristics of high speed and high efficiency. The photometric stereo method is a potential 3D measurement method with high speed and low cost. However, the traditional photometric stereo method is unsuitable for dynamic scenes due to its initial design for static scenes. In this paper, we propose a novel dynamic photometric stereo method based on an image registration method. To achieve fast speed and high efficiency, we reduce the computational cost by automatically generating regions of interest (ROI). Additionally, we innovatively map the depth information (the surface normal vectors) to a mean curvature map of the surface and use it to detect defects, which combines the robustness of 3D methods and the fast speed of 2D methods. We designed experiments and the results showed that our method can detect defects on the surfaces of aluminum alloy castings accurately and robustly in an online manner. This paper also aims to reveal the importance of utilizing multidimensional information in high-speed online inspections.

Automatic quantification of concrete cracks via multistage image filtration and trajectory-based local binarization

Ensuring the structural integrity of civil infrastructure and maintaining public safety necessitates accurate and reliable periodic inspection of cracks on concrete surfaces. Image-processing crack detection techniques offer an automated cost-effective alternative for identifying and sizing of cracks. However, the distinctive occurrence of cracks and the presence of environmental noises significantly limit detection capabilities. The method proposed in this study utilizes suitable image enhancement techniques based on the variance in gray pixel intensity in the input image. Moreover, the polynomial curve fit noise detrend technique is used to filter out low frequency noises, including shadows and nonuniform illumination. Symmetry and geometric-based filtration are also sequentially implemented for further crack detection. Furthermore, a trajectory-based local binarization is applied to recover broken crack features due to gray-pixel intensity variations. To achieve accurate and consistent results, the fundamental causes of error in crack sizing are identified and addressed. The proposed algorithm's effectiveness is validated using approximately 80 real images collected from various references. The results demonstrate promising outcomes in actual crack detection and sizing.

Automated two-dimensional geometric model reconstruction from point cloud data for construction quality inspection and maintenance

Despite the availability of 3D digital models, 2D floor plans remain extensively used for quality inspection and maintenance as they offer firsthand information. While laser scanners enable efficient capture and reconstruction of real-world scenes, challenges arise in accurately extracting building geometry from laser scanning data due to the loss of geometric features. This paper describes a method for accurately reconstructing 2D geometric drawings of built facilities using laser scanning data. These techniques involve transforming the dimension of 3D data into 2D and displaying the registered data as pixels to extract solid lines that represent wall structures. By employing dimensionality transformation and pixelation techniques, the method supports reliable quality inspection and maintenance processes, overcoming the challenges of extracting precise geometry from laser scanning data. This paper contributes to the automated extraction of geometric features from point clouds and inspires the future development of fully automated 2D CAD and 3D BIM in alignment with Scan-to-BIM.

Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection

The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works.

Image-Based Inspection System of Firefighting Facilities Using Augmented Reality

With the rapid development of digital-based industrial structures, the construction industry is also rapidly adopting smart-based technologies and unmanned automation, leading to an increase in the construction of new buildings. To protect against fire, disasters, and other emergency situations, installing firefighting facilities is mandatory , and continuous management is required. However, owing to the increase in new buildings, the installation of firefighting facilities is increasing, but the manpower to manage and inspect these firefighting facilities is lacking. In this paper, a technical improvement plan is proposed for the existing analog method of facility inspection. By combining deep learning algorithm and augmented reality, a fire protection inspection system is developed and inspections are performed through wearable devices. To train the object detection model, we built a dataset of five types of firefighting facilities. The trained YOLO v4 model achieved a performance accuracy of 93.6%. The proposed system is expected to perform more convenient and reliable inspections and to help with fire safety management by enabling both fire safety professionals and the general public to learn about fire protection inspection systems and perform self-inspections.

Smarthub for supervising system for resource exploration and pollution control in deep-water and coastal areas based on ICT technologies

PurposeThis study aims to explore the need for highly technological complexes for control and monitoring, as well as, new concepts and methodologies for maritime resource exploration and exploitation, which are in great demand nowadays.Design/methodology/approachThis paper provides an analysis of demand, means of creation and development of the methodology and infrastructure for global monitoring, pollution control and supervision of smart systems for activities in exploration, future resource exploitation in deep-water and coastal areas based on Smarthub architecture, Unmanned Aircraft System (UAS), Continuous Acquisition and Life-Cycle Support (CALS) and Blockchain technologies.FindingsObservational, experimental, simulation, derivational, hybrid descriptive and analytical models, as well as, surrogate models were created, analyzed and implemented for assigned tasks realization. Concept of distributed system for marine environmental monitoring, control and supervising as pilot technology in the context of Technology Readiness Levels (TRL) 3–5 was designed and evaluated.Originality/valueThe activities described in this article should be realized in the design and development of a complex, reliable, robust and sustainable monitoring and inspection system for the control and evaluation of the impact and risk assessment of the exploration and future exploitation of maritime resources.

Ultrasonic examination of a thin, textured metal plate with Penrose tile structure consisting of pitched indentations

A feasibility study is presented on the experimental application of ultrasound to examine rolled stainless steel plates having equidistant surface textures in two directions in the form of Penrose tiles. The specific problem of interest is investigating surface profile quality in terms of its equidistance and depth to monitor the manufacturing process. The goal is to eventually replace current time-consuming optical examination procedures with a reliable and rapid ultrasonic inspection technique. Two practical experimental setups are discussed and compared in this work: examining frequency spectra obtained from normal incidence pulse-echo measurements, and those obtained at Laue angle incidence. A thorough survey of ultrasonic methods precedes the experimental results to investigate such surfaces from a historical perspective.

Image-based and risk-informed detection of Subsea Pipeline damage

As one of the most important assets in the transportation of oil and gas products, subsea pipelines are susceptible to various environmental hazards, such as mechanical damage and corrosion, that can compromise their structural integrity and cause catastrophic environmental and financial damage. Autonomous underwater systems (AUS) are expected to assist offshore operations personnel and contribute to subsea pipeline inspection, maintenance, and damage detection tasks. Despite the promise of increased safety, AUS technology needs to mature, especially for image-based inspections with computer vision methods that analyze incoming images and detect potential pipeline damage through anomaly detection. Recent research addresses some of the most significant computer vision challenges for subsea environments, including visibility, color, and shape reconstruction. However, despite the high quality of subsea images, the lack of training data for reliable image analysis and the difficulty of incorporating risk-based knowledge into existing approaches continue to be significant obstacles. In this paper, we analyze industry-provided images of subsea pipelines and propose a methodology to address the challenges faced by popular computer vision methods. We focus on the difficulty posed by a lack of training data and the opportunities of creating synthetic data using risk analysis insights. We gather information on subsea pipeline anomalies, evaluate the general computer vision approaches, and generate synthetic data to compensate for the challenges that result from lacking training data, and evidence of pipeline damage in data, thereby increasing the likelihood of a more reliable AUS subsea pipeline inspection for damage detection.

A comparative evaluation and real-time measurement of failures in solar power plants by thermal imaging in Turkey

The demand for electricity generated from solar energy in Turkey is increasing daily. For sustainability in energy, a good design, periodic operational maintenance and instant monitoring of the field all play important roles when generating photovoltaic power. Fast and on-site detection of failures in photovoltaic fields is required to ensure long-term, high-efficiency photovoltaic power generation. On-site testing of solar power systems is useful for evaluating the performance of facilities and identifying potential problems. Thermal imaging of photovoltaic modules and electrical components is the most reliable inspection method compared with other methods. In this article, thermal imaging results from eight different Turkish solar power plants (23.5 MWp in total) are reported. We found that the most common difficulties identified during field tests could be classified according to the type of failure. This experiment demonstrates the efficiency and effectiveness of the thermal imaging approach, both in terms of accuracy and processing time. Passive bypass diodes, connection failures and hot spots in photovoltaic modules and strings, abnormally exploded fuses, and panel cracking and breakage were the main causes of these failures.

The relative area score for sublingual varices reliability measurement: a diagnostic study

BackgroundSublingual varices (SV) and their predictive potential for other clinical parameters is a much studied topic in oral medicine. SVs have been well studied as predictive markers for many common diseases such as arterial hypertension, cardiovascular disease, smoking, type 2 diabetes mellitus and age. Despite many prevalence studies, it is still unclear how the reliability of SV inspection affects its predictive power. The aim of this study was to quantify the inspection reliability of SV.MethodsIn a diagnostic study, the clinical inspection of 78 patients by 23 clinicians was examined for the diagnosis of SV. Digital images of the underside of the tongue were taken from each patient. The physicians were then asked to rate them for the presence of sublingual varices (0/1) in an online inspection experiment. Statistical analysis for inter-item and inter-rater reliability was performed in a τ-equivalent measurement model with Cronbach's alpha and Fleiss κ.ResultsThe interrater reliability for sublingual varices was relatively low with κ = 0.397. The internal consistency of image findings for SV was relatively high with α≈ 0.937. This shows that although SV inspection is possible in principle, it has a low reliability R. This means that the inspection finding (0/1) of individual images often cannot be reproduced stably. Therefore, SV inspection is a difficult task of clinical investigation. The reliability R of SV inspection also limits the maximum linear correlation {r}_{max} of SV with an arbitrary other parameter Y. The reliability of SV inspection R = 0.847 limits the maximum correlation to {r}_{max} (SV, Y) = 0,920—a 100% correlation was a priori not achievable in our sample. To overcome the problem of low reliability in SV inspection, we propose the RA (relative area) score as a continuous classification system for SV, which normalises the area of visible sublingual veins to the square of the length of the tongue, providing a dimensionless measure of SV.ConclusionsThe reliability of the SV inspection is relatively low. This limits the maximum possible correlation of SV with other (clinical) parameters. SV inspection reliability is an important indicator for the quality of SV as a predictive marker. This should be taken into account when interpreting previous studies on SV and has implications for future studies. The RA score could help to objectify the SV examination and thus increase its reliability.