Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

Magnetic flux leakage (MFL) detection methods are widely used to detect pipeline defects. However, it is limited by the detection orientation and magnetization. Besides, bulky excitation systems are incapable of adapting to the complex detection environments. This article proposes a new electromagnetic structured coupling sensing of merging alternating current field measurement and MFL within a multiparameter system for different types of pipeline defects detection. In particular, a novel electromagnetic coupling sensor structure is proposed, which enables simultaneous interaction between the excitation modes of yoke and coil. Magnetic yoke is integrated to magnetizing the axial pipeline to detect the circumferential surface and subsurface defects while the coil excites the circumferential uniform alternating current field and recognizes the axial defect. The novel structured sensing is highly sensitive to the detection of both surface and subsurface defects. Simulation and experiments on defects in several samples have been conducted to validate the reliability and efficiency of the proposed system.

Eddy current (EC) detection technique has been applied for the inspection of small-diameter pipelines whereas it retains the limitation in detecting defects on the outer wall of the pipeline. This paper proposes a new instrumentation of cross electromagnetic eddy current sensing for internal pipeline inspection, The technique integrates the principles of remote field eddy current and near field eddy current, and involves exciting two types of magnetic fields inside the pipeline. The first magnetic field is oriented perpendicular to the pipe wall, which allows for the detection of inner wall defects. The second magnetic field propagates along the pipeline direction, enabling the detection of outer wall defects. By combining these two magnetic fields, the proposed technique enables simultaneous detection of both inner and outer wall defects in small-diameter buried pipelines. A detection platform for an experimental laboratory system has been built, and an internally integrated system developed for pipelines with an 8-inch diameter. By conducting inspections on various pipelines, defects on both the inner and outer walls were successfully detected. Results has validated the reliability and efficiency of the proposed detector.

In-pipeline inspection is an important precontrol method to ensure the safety of oil and gas pipeline transportation. This article proposes an electromagnetic in-pipe detector based on passive resonance-enhanced differential planar coils to detect defects on the inner surface of pipes. Both qualitative and quantitative analyses of pipeline defects and damage are developed. The introduction of passive resonant coils is shown to significantly improve the detection capability of the sensor. This is coupled with the establishment of a theoretical derivation model of the proposed structure. The hardware platform of the laboratory system has been built, and an eddy current internal detector suitable for 8-in-diameter pipes is developed and integrated into the system. Numerical simulations and experimental verifications on flat defects and pipe defects have been undertaken. The obtained results have shown that the real defects have been correctly detected, and the system is effective, reliable, and efficient.

In the realm of electromagnetic nondestructive testing (NDT), accurately identifying and characterizing flaws within various materials is crucial for ensuring structural integrity. This article proposes a novel intelligent electromagnetic perception framework that combines physical and digital artificial intelligence to address the sensitivity and accuracy limitations inherent in conventional electromagnetic NDT. Unlike traditional passive data acquisition methods, the proposed system integrates a physical electromagnetic neural network and a physics-aware reinforcement learning algorithm to adaptively optimize electromagnetic field sensing parameters in real-time, significantly enhancing sensitivity in regions close to defects. On the digital side, a sensor-informed diffusion model reconstructs high-resolution images from low-resolution optimal sensitivity sensor data, allowing for detailed analysis of defect contours and depths. Experimental results demonstrate a maximum sensitivity improvement of 105.8% and a minimum defect quantification of 0.2 mm, exceeding the performance of established electromagnetic NDT techniques. This innovative framework combines adaptive electromagnetic field focusing with advanced image reconstruction, establishing a new benchmark in real-time, high-precision defect detection. In addition, it is offering valuable applications in pipeline inspection, aerospace, and automotive industries.