Significance Pipelines are fundamental infrastructure for energy transportation, and their safety is directly related to industrial production and environmental protection. Corrosion remains a primary cause of pipeline failure, highlighting the importance of efficient and reliable non-destructive testing technologies. Infrared thermography is a representative non-destructive testing technique. Based on the presence or absence of an external excitation source, it can be categorized as passive or active thermography. Passive thermography relies on the self-emission of the monitored target, while active thermography introduces external excitation to create thermal contrast, making it more suitable for the early detection of corrosion and cracks in industrial pipelines. Among various active methods, eddy current pulsed thermography combines the deep penetration of electromagnetic induction with the high-resolution visualization of infrared imaging, demonstrating strong potential for pipeline corrosion detection. This technology possesses significant advantages such as non-contact operation, high detection speed, high sensitivity, and the ability to perform inspections through non-conductive insulation layers, becoming a key means of ensuring pipeline integrity.

Progress The mechanism and components of the eddy current pulsed thermography testing system in pipeline applications are first introduced. The system primarily consists of a high-frequency induction heating power supply, an induction coil, an infrared thermal imager, and synchronization control and data processing units. During testing, the induction coil generates eddy currents in the metallic pipeline wall. The presence of corrosion or cracks distorts the eddy current distribution, resulting in localized heat accumulation. The technology is then systematically classified according to coil configuration and detection mode. To address the cylindrical structure of pipelines, researchers have developed arc array coils and flexible coils to enhance magnetic field coupling. Based on the excitation signal, the technique can be divided into pulsed excitation, lock-in modulation, and phase-based modes. Since this technology was applied to pipeline inspection, research has progressed from basic defect detection to precise characterization under complex conditions. To address challenges such as insulation layer interference, lift-off effects, and non-uniform surfaces, extensive innovation has taken place. For insulated pipelines, corrosion under insulation can now be effectively identified by optimizing excitation frequency and increasing power density. In quantitative evaluation, approaches such as principal component analysis, skewness analysis, and wavelet transform have been employed to extract thermal features, enabling accurate measurement of pipeline wall thinning. Additionally, convolutional neural networks integrated with deep learning have been utilized to automatically classify pitting and uniform corrosion, greatly improving the intelligence level of detection. Currently, this technology is widely used for the routine inspection of long-distance oil and gas pipelines, power plant boiler tubes, and urban heating networks.

Conclusions and Prospects Currently, investigations on complex irregular sections, such as pipeline elbows and tees, remain limited, and interference from variations in surface emissivity still requires further mitigation. Most research is focused on qualitative identification in laboratory conditions; quantitative accuracy and three-dimensional morphology reconstruction in real engineering environments still require significant improvement. The size and power demands of testing equipment also restrict the automation of long-distance pipeline inspection using eddy current pulsed thermography. With the development of intelligent manufacturing and the Industrial Internet, pipeline corrosion detection is undergoing a transformation from manual interpretation to automated evaluation. Eddy current pulsed thermography technology is expanding from simple surface damage identification to the quantitative detection of internal wall thinning, and from two dimensional thermal image analysis to three dimensional defect reconstruction. In addition, the development of lightweight induction heating devices with high environmental adaptability and deep learning algorithms integrated with physical models will provide strong support for achieving high-precision quantitative evaluation and life prediction of pipeline corrosion.