Search Results (67)

Cultural heritage (CH) relics are irreplaceable records of human civilization, encompassing diverse historical, technological, and artistic achievements. Extracting their structural and compositional information without affecting their physical integrity is a critical challenge. This review summarizes recent advances in non-destructive techniques (NDTs) for CH analysis and emphasizes the balance between the depth of analysis and conservation ethics. Techniques are broadly categorized into spectrum-based, X-ray-based, and digital-based methods. Spectroscopic techniques such as Fourier transform infrared (FTIR), Raman, and nuclear magnetic resonance (NMR) spectroscopy provide molecular-level insights into organic and inorganic components, often requiring minimal or no sampling. X-ray-based techniques, including conventional and spatially resolved XRD/XRF and total reflection XRF (TRXRF), provide powerful means for crystal and elemental analysis, including in situ pigment identification and trace material analysis. Digital-based methods include high-resolution imaging, three-dimensional modeling, data fusion, and AI-driven diagnosis to achieve the non-invasive visualization, monitoring, and virtual restoration of CH assets. This review highlights a methodology shift from traditional molecular-level detection to data-centric and AI-assisted diagnosis, reflecting the paradigm shift in heritage science. Full article

As the world increasingly gravitates towards green, environmentally friendly and low-carbon lifestyles, wind power has become one of the most technologically established renewable energy sources. However, with the continuous increase in their output power and height, wind turbine towers are subjected to higher-intensity alternating wind loads. This makes critical components more prone to fatigue failure, potentially leading to major accidents such as tower buckling or turbine collapse. High-strength bolts play a vital role in supporting towers but are susceptible to fatigue crack initiation under long-term cyclic loading, necessitating regular inspection. Types of wind turbine bolts mainly include high-strength bolts, stainless steel bolts, anchor bolts, titanium alloy bolts, and adjustable bolts. These bolts are distributed across different parts of the turbine and perform distinct functions. Among them, high-strength bolts in the tower are particularly critical for structural support, demanding prioritized periodic inspection. Compared to destructive offline inspection methods requiring bolt disassembly, non-destructive testing (NDT) has emerged as a trend in defect detection technologies. Therefore, this review comprehensively examines various types of NDT techniques for wind turbine towers’ high-strength bolts, including disassembly inspection techniques (magnetic particle inspection, penetration inspection, intelligent torque inspection, etc.) and non-disassembly inspection techniques (ultrasonic inspection, radiographic inspection, infrared thermographic inspection, etc.). For each technique, we analyze the fundamental principles, technical characteristics, and limitations, while emphasizing the interconnections between the methodologies. Finally, we discuss potential future research directions for bolt defect NDT technologies. Full article

In view of the problems of poor safety, slow detection speed, and low accuracy of existing nondestructive testing (NDT) technologies, such as X-ray methods and ultrasonic detection in detecting electron beam weld defects in aluminum alloys, this study proposes a weak magnetic NDT method based on the geomagnetic field. Firstly, the finite element analysis method was used to establish a simulation model of aluminum alloy electron beam welding defects, and the distribution characteristics of the magnetic field around weld defects, such as cracks and pores, were obtained. Then, the magnetic anomaly signal at the crack weld was identified by combining the wavelet transform and the least squares method. Finally, experimental tests show that the proposed method can safely, quickly, and accurately detect the defects of aluminum alloy electron beam welds. Full article

This study proposes a novel non-destructive methodology for assessing structural integrity in liquefied natural gas (LNG) carrier cargo containment systems (CCSs), addressing limitations of conventional inspection techniques like visual inspection and vacuum box testing. The method leverages strain-induced martensitic transformation (SIMT) in austenitic stainless steel (SUS304L), widely used in CCS membranes, quantifying magnetic permeability increase via a Feritscope to evaluate deformation history and damage. To analyze SUS304L SIMT behavior, uniaxial tensile (UT) and equi-biaxial tensile (EBT) tests were conducted, as these stress states predominate in CCS membranes. Microstructural evolution was examined using X-ray diffraction (XRD) and electron backscatter diffraction (EBSD), allowing a quantitative assessment of the transformed martensite volume fraction versus plastic strain. Subsequently, Feritscope measurements under the same conditions were calibrated against the XRD-measured martensite volume fraction for accuracy. Based on testing, this study introduces three complementary Feritscope approaches for evaluating CCS health: outlier detection, quantitative damaged area analysis, and time-series analysis. The methodology integrates data-driven quantitative assessment with conventional qualitative inspection, enhancing safety and maintenance efficiency. Full article

Microwave nondestructive testing (MNDT) has shown great potential in detecting defects in various materials. This is due to it being safe and noninvasive. Safety is essential for the operators as well as the specimens being tested. Being noninvasive is important in maintaining the health of critical structures and components across various industries. In this paper, a review of MNDT methods is given with a comparison against other NDT techniques. First, the latter techniques are described, namely testing using a dye penetrant, ultrasound, eddy currents, magnetic particles, or radiography. Next, an overview of various microwave NDT methods is provided through a review of the applications, advantages, and limitations of each technique. Further, a detailed review of emerging MNDT techniques like microwave microscopy, active microwave thermography, and chipless radio frequency identification is presented. Next, a brief description of current and emerging algorithms employed in MNDT is discussed, with emphasis on those using artificial intelligence. By providing a comprehensive review, this article aims to shed light on the current state of MNDT, thus serving as a reference for subsequent innovations in this rapidly evolving domain. Full article

Cement poles serve as supporting components for transmission lines and are widely used in medium- and low-voltage transmission networks. The main rebar is the primary load-bearing structure of the pole, and the accurate measurement of its diameter and embedment depth is crucial for quality control and safety assessment. However, existing non-destructive testing methods lack the accuracy of quantifying the internal main rebar of cement poles, and the measurement process is complex, cumbersome, and inefficient. To address this issue, this paper proposes a magnetic rotation-based detection method for measuring the diameter and embedment depth of the main rebar within cement poles. A specially designed H-type magnetic excitation structure is proposed, coupled with a detection technique utilizing rotating permanent magnets. The magnetic induction intensity data were acquired at seven distinct rotation angles using sensors, and the collected data were subsequently combined with a CNN-LSTM model to invert the diameter and embedment depth of the main rebar. The experimental results indicate that the method significantly improved the measurement accuracy compared with the condition of fixed magnetic excitation, with reductions in root mean square error (RMSE) of 46.71% and 35.57% for the diameter and embedment depth measurements, respectively. This method provides a robust, efficient, and accurate solution for quantifying the main rebar within cement poles, addressing the challenge associated with the quality assessment and health monitoring of these structures. Full article

Water transport pipelines in the mining industry face significant corrosion challenges due to extreme environmental conditions, such as arid climates, temperature fluctuations, and abrasive soils. This study evaluates the effectiveness of three advanced inspection technologies—Guided Wave Ultrasonic Testing (GWUT), Metal Magnetic Memory (MMM), and In-Line Inspection (ILI)—in maintaining pipeline integrity under such conditions. A structured methodology combining diagnostic assessment, technology research, and comparative evaluation was applied, using key performance indicators like detection capability, operational impact, and feasibility. The results show that GWUT effectively identifies surface anomalies and wall thinning over long pipeline sections but faces depth and diameter limitations. MMM excels at detecting early-stage stress and corrosion in inaccessible locations, benefiting from minimal preparation and strong market availability. ILI provides comprehensive internal and external assessments but requires piggable pipelines and operational adjustments, limiting its use in certain systems. A case study of critical aqueducts of mining site water supply illustrates real-world technology selection challenges. The findings underscore the importance of an integrated inspection approach, leveraging the complementary strengths of these technologies to ensure reliable pipeline integrity management. Future research should focus on quantitative performance metrics and cost-effectiveness analyses to optimize inspection strategies for mining infrastructure. Full article

This article evaluates two essential non-destructive electromagnetic techniques, magnetic flux leakage (MFL) and eddy current (EC) methods, and their effectiveness in assessing the basic parameters of reinforced concrete (RC). The study compares both systems’ hardware and software components, emphasizing the adaptations implemented to tailor the methods for evaluating RC structures. Subsequently, the measurement results are analyzed, and association rules are extracted to demonstrate the relationships between variations in the physical parameters of the tested structure and the features of the measured waveforms. Finally, similar identification models are implemented, and the obtained identification results are compared. The paper documents and details all phases of this research. The findings indicate that while the operational principles of both methods are similar, the techniques differ significantly in terms of their measurement systems’ complexity and usability. The eddy current (EC) method exhibits superior spatial resolution, whereas the magnetic method is more straightforward and offers a greater effective range and favorable association rules. Consequently, it is recommended that both techniques be utilized for different structures and in varying contexts. The techniques’ advantages, disadvantages, and limitations are discussed in this work and supported by the measurement results. Full article

This paper aims to present a non-destructive magnetic method developed for simultaneously identifying key parameters in reinforced concrete (RC) structures. This method is designed to evaluate concrete structures containing dense reinforcement meshes. The development process of the system is presented step by step. First, the hardware layer of the system is discussed and documented. The proposed modifications to the excitation system enhanced the method’s effectiveness compared to the conventional Magnetic Flux Leakage (MFL) technique. Next, this paper presents the extracted association rules that connect the parameters of the RC structure to the properties of resultant waveforms. The relations are verified with measurement results. Finally, the identification of the model’s design, implementation, and deployment processes are presented. A universal methodology for non-destructive testing investigations was developed and is described throughout the research. Full article

Multi-layer conductive structures, especially those with features like bolt holes, are vulnerable to hidden corrosion and cracking, posing a serious threat to equipment integrity. Early defect detection is vital for implementing effective maintenance strategies. However, the subtle signals produced by these defects necessitate highly sensitive non-destructive testing (NDT) techniques. Analytical modeling plays a critical role in both enhancing defect-detection capabilities and guiding the design of highly sensitive sensors for these complex structures. Compared to the finite element method (FEM), analytical approaches offer advantages, such as faster computation and high accuracy, enabling a comprehensive analysis of how sensor and material parameters influence defect detection outcomes. This paper introduces a novel T-core eddy current sensor featuring a central air gap. Utilizing the vector magnetic potential method and a truncated region eigenfunction expansion (TREE) method, an analytical model was developed to investigate the sensor’s interaction with multi-layer conductive materials containing a hidden hole. The model yielded closed-form expressions for the induced eddy current density and coil impedance. A comparative study, implemented in Matlab, analyzed the eddy current distribution generated by T-core, E-core, I-core, and air core sensors under identical conditions. Furthermore, the study examined how the impedance of the T-core sensor changed at different excitation frequencies between 100 Hz and 10 kHz when positioned over a multi-layer conductor with a hidden air hole. These findings were then compared to those obtained from E-core, I-core, and air-core sensors. The analytical results were validated through finite element simulations and experimental measurements, exhibiting excellent agreement. The study further explored the influence of T-core design parameters, including the air gap radius, dome radius, core column height, and relative permeability of the T-core material, on the inspection sensitivity. Finally, the proposed T-core sensor was used to evaluate crack and hole defects in conductors, demonstrating its superior sensitivity compared to I-core and air core sensors. Although slightly less sensitive than the E-core sensor, the T-core sensor offers advantages, including a more compact design and reduced material requirements, making it well-suited for inspecting intricate and confined surfaces of the target object. This analytical model provides a valuable tool for designing advanced eddy current sensors, particularly for applications like detecting bolt hole defects or measuring the thickness of non-conductive coatings in multi-layer conductor structures. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

Differential coils are frequently implemented in inductive sensing systems. They can be considered as a single coil that is made up of two or more subcoils, wound in series opposition. They can be used on the transmit or receive side of measurement systems, and, if designed correctly, ensure no coupling between coils under background conditions. By cancelling background coupling, the receive electronics only needs to be able to measure the change in coupling produced by a target. This allows for a more efficient use of the dynamic range, and for larger receive-side amplifier gain, thereby improving SNR. When subcoils are not electrically similar, it can be hard to engineer the coil to be perfectly balanced across a wide bandwidth. This paper presents an analytical model of a resonant differential coil pair that is tested and applied on a planar metal detector for the detection of buried objects. The model demonstrates the capability to balance an arbitrary differential coil pair, which has a broad applicability across a range of inductive sensor applications such as metal detection and non-destructive testing. The method is applied to the practical system. The results show that the correction resulting from this method ensures a stable balance across a significantly enhanced bandwidth. In the case studied here, the bandwidth of the experimental system is increased from 20 kHz to 90 kHz. Full article

Both microstructure and stress affect the structure and kinematic properties of magnetic domains. In fact, microstructural and stress variations often coexist. However, the coupling of microstructure and stress on magnetic domains is seldom considered in the evaluation of microstructural characteristics. In this investigation, Magnetic incremental permeability (MIP) and magnetic Barkhausen noise (MBN) techniques are used to study the coupling effect of characteristic microstructure and stress on the reversible and irreversible motions of magnetic domains, and the quantitative relationship between microstructure and magnetic domain characteristics is established. Considering the coupling effect of microstructure and stress on magnetic domains, a patterned characterization method of microstructure and stress is innovatively proposed. Pattern recognition based on the Multi-layer Perceptron (MLP) model is realized for microstructure and stress with an accuracy rate higher than 97%. The results show that the pattern recognition accuracy of magnetic domain features and micro-magnetic features simultaneously as input parameters is higher than that of micro-magnetic features alone as input parameters. Full article

A new nondestructive inspection method, the magnetic hammer test (MHT), which uses a compact and highly sensitive tunnel magnetoresistance (TMR) sensor, is proposed. This method complements the magnetic flux leakage method and eliminates the issues of the hammer test. It can therefore detect weak magnetic fields generated by the natural vibration of a pipe with a high signal-to-noise ratio. In this study, several steel pipes with different wall thicknesses were measured using a TMR sensor to demonstrate the superiority of MHT. The results of the measurement show that wall thickness can be evaluated with the accuracy of several tens of microns from the change in the natural vibration frequency of the specimen pipe. The pipes were also inspected underwater using a waterproofed TMR sensor, which demonstrated an accuracy of less than 100 μm. The validity of these results was by simulating the shielding of magnetic fields and vibration of the pipes with the finite element method (FEM) analysis. The proposed noncontact, fast, and accurate method for thickness testing of long-distance pipes will contribute to unmanned, manpower-saving nondestructive testing (NDT) in the future. Full article

Magnetic Particle Inspection (MPI) is one of the most used methods in Non-Destructive Testing (NDT), allowing precise and robust defect detection on industrial-grade manufactured parts. However, human controllers perform this task in full black environments under UV-A lighting only (with safety glasses) and use chemical products in a confined environment. Those constraints tends to lower control performance and increase stress and fatigue. As a solution, we propose an AI-based assistive machine (called “PARADES”) inside the hazardous environment, remotely manipulated by a human operator, outside of the confined area, in cleaner and safer conditions. This paper focuses on the development of a complete industrial-grade AI machine, both in terms of hardware and software. The result is a standalone assistive AI-based vision system, plug-and-play and controller-friendly, which only needs the usual power supply 230 V plug that detects defects and measures defect length. In conclusion, the PARADES machines address for the first time the problem of occupational health in MPI with an industrial standalone machine which can work on several parts and be integrated into current production lines. Providing cleaner and healthier working conditions for operators will invariably lead to increased quality of detection. These results suggest that it would be beneficial to spread this kind of AI-based assistive technology in NDT, in particular MPI, but also in Fluorescent Penetrant Testing (FPT) or in visual inspection. Full article