Electromagnetic Acoustic Transducer (EMAT) has become one of the fastest-growing solutions for pipeline weld inspection over the past decade due to its non-contact advantage. One primary problem of EMAT is that it has relatively lower energy transition efficiency compared to widely used piezoelectric transducers, coupled with the effect of lift-off and the non-uniformity issue of welding material, the Signal-to-Noise Ratio (SNR) can be significantly restricted. This brings great difficulty in interpreting the EMAT signal measured from pipeline girth welds. To overcome this challenge, this paper presents a deep learning-based ultrasonic pattern recognition method to identify the pipeline girth weld cracking automatically. The proposed method utilizes a deep Convolution Neural Network (CNN) integrated with a pre-trained Support Vector Machine (SVM) classifier to extract the high-level features from the time-frequency representation of A-scan signals measured by bulk-wave EMAT and classify these signals into defective or non-defective groups. To validate the proposed method, a set of experiments is carried out to classify A-scan signals measured from the girth welds of an ex-service type 813-X70 gas pipeline. A comparative investigation is also undertaken to demonstrate the superiority of the proposed method against the conventional ultrasonic pattern recognition methods for evaluation.

Leakage of stray current can cause serious problems by accelerating the corrosion process of a buried pipeline in the subway. A multi-physical finite element model of the DC-subway traction system was established in this study, and the dynamic process of the stray current corrosion on buried pipeline was calculated according to the real time traction conditions. In this study, the corrosion rate variation of stray current is evaluated, and the corrosion trend of stray current is quantitatively calculated. The model simulation shows that the stray current corrosion is significantly higher than other processes during the acceleration process of the subway locomotive. And the potential of the buried pipeline reaches a maximum value when an up-rush of traction current occurs. The corrosion is mainly concentrated in the anode region of the buried pipeline, and the closer the buried pipeline is to the rail the more serious the corrosion is. A corrosion experiment of N20 carbon steel was carried out and verified by finite element model. The results further show that the finite element model can quantitatively calculate and predict the mass loss of buried metal caused by stray current corrosion.

This study presents a modified one–side pitch–catch electromagnetic acoustic transducer (EMAT), the optimized design improves the horizontal static magnetic field excitation and helps to enhance the generation and reception of the Lamb wave. A coupling model is applied to characterise and analyse the new design in detail. The model indicates that the optimized magnetic field with an increased peak flux density compared to the traditional magnetic configuration of EMAT. As a result, the transduction efficiency of the one–side pitch–catch design is dramatically improved ingeneration and reception process. Experiments verify that a measurement system using the modified one–side pitch–catch design is improved by a factor of 3.3, when compared to conventional one–side pitch–catch design.

High-strength bolts are crucial load-bearing components of wind turbine towers. They are highly susceptible to fatigue cracks over long-term service and require timely detection. However, due to the structural complexity and hidden nature of the cracks in wind turbine tower bolts, the small size of the cracks, and their variable propagation directions, detection signals carrying crack information are often drowned out by dense thread signals. Existing non-destructive testing methods are unable to quickly and accurately characterize small cracks at the thread roots. Therefore, we propose an ultrasonic phased array element arrangement method based on the Fermat spiral array. This method can greatly increase the fill rate of the phased array with small element spacing while reducing the effects of grating and sidelobes, thereby achieving high-energy excitation and accurate imaging with the ultrasonic phased array. This has significant theoretical and engineering application value for ensuring the safe and reliable service of key wind turbine components and for promoting the technological development of the wind power industry.

Stray current directly affects the regular operation of electrical equipment and facilities in the subway DC traction power supply system. Therefore, it is worthwhile to study the stray current distribution characteristics during train operation and the quantitative corrosion of buried pipelines. This paper introduces the traction characteristics of power carriages and power wheelsets of subway vehicles into the DC traction process. A finite element model considering the dynamic distribution of stray current under the actual operation of subway vehicles is established. The interference characteristics of stray current and the contribution of power sources under the multiparticle model are analyzed. The rail insulation damage caused by long service time and the quantitative calculation of rail and buried pipeline corrosion is considered. The model results show that the stray current in the buried pipeline under the multiparticle model is more accurate and more suitable for the protection in the actual subway. The quantitative corrosion of the buried pipeline is stronger than the partial insulation damage environment when the rail is not insulated. The rail and buried pipeline corrosion at both ends of the insulation damage position is relatively severe. The stray current distribution model established in this paper gives full play to the solution advantages of the finite element method and provides a new idea for the quantitative calculation of buried pipeline corrosion.

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.