Wire ropes (WRs) are widely used in various areas, such as tourist cables, mining, and elevators. Thus, it is very important to detect broken wires (BWs) to prevent safety issues. To guarantee the structural health of WRs, this paper proposes the use of induction thermography (IT) to detect the BWs. An image segmentation algorithm based on scale morphology is proposed to eliminate the background influence of non-uniform heating and visualize the BWs. Further, the area of the BW region, after the image binarization, subtraction, and filtering, is extracted as a feature to quantify the number of BWs. Results show that the proposed methods can effectively visualize and quantify the broken region within 1 BWs.

In order to improve the sensitivity of online magnetic flux leakage (MFL) testing for steel pipe, a sensing method based on the magnetic guiding effect is proposed and investigated in this paper. Compared to the conventional contact sensing method using a non-ferromagnetic support, the proposed method creatively utilizes a ferromagnetic one to guide more magnetic flux to leak out. Based on Hopkinson’s law, the principle of the magnetic guiding effect of the ferromagnetic support is theoretically illustrated. Then, numerical simulations are conducted to investigate the MFL changes influenced by the ferromagnetic support. Finally, the probe based on the proposed method is designed and developed, and online MFL experiments are performed to validate the feasibility of the proposed method. Online tests show that the proposed sensing method can greatly improve the MFL sensitivity.

Nowadays, scanning induction thermography (SIT) could be efficient and practical for the inspection of elongated ferromagnetic specimens in the manufacturing process. Owing to the relative movement between the specimen and excitation coil, defect visualization and depth quantification in SIT remain challenging. Focusing on these two challenges, surface-breaking notches with different depths in a steel plate at different scanning speeds have been studied in this paper. A visualization method, including image reconstruction and Wiener filter, is proposed to realize defect visualization within 600 mm/s. Then, thermal features are extracted to realize depth quantification for notches within 3.0 mm. In addition, the effect of scanning speed on the SIT detectability is investigated and it is found that the signal-to-noise ratio (SNR) decreases significantly with increasing scanning speed.

Magneto-acousto-electrical tomography (MAET) is a powerful tool for numerous applications, including medical imaging and process tomography. In these applications, and to enable precise reconstruction of images of a given process using MAET, high-quality measurements must be obtained from the target of interest. It is thus obvious that the magnetic field excitation used in these measurements plays an important role in this task. However, the existing reports in the literature to date have usually assumed that the static magnetic field is a uniform field, which is challenging to achieve in practice. In this article, we circumvent this challenge and present a comprehensive mathematical model for MAET in the presence of nonuniform magnetic fields. In the proposed model, a relationship is established between the measurement signal and the reciprocal current density. The conductivity distribution is obtained via a simulation model and compared with the second norm error of the images and the various uniformities of the nonuniform magnetic fields obtained from the imaging results. The model is then validated for different test cases, which range from a simple phantom with three layers to a phantom with a cylindrical geometry. It is concluded that the use of a nonuniform magnetic field can lead to significant changes and improvements in the reconstruction of the conductivity, thus making it possible to use this technique in bedside detection applications.