A pulsed eddy current sensor for UAV deployed pipe thickness measurement

Zhao, Taiyi and Watson, Robert and Zhang, Dayi and McMillan, Ross and Galbraith, Walter and MacLeod, Charles Norman and Mohseni, Ehsan and Dobie, Gordon (2024) A pulsed eddy current sensor for UAV deployed pipe thickness measurement. IEEE Sensors Journal. ISSN 1530-437X (In Press) (https://doi.org/10.1109/JSEN.2024.3450193)

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Abstract

The necessity to inspect essential infrastructure such as oil and gas pipelines for wear, and deterioration highlights the critical role of enhancing Non-Destructive Testing (NDT) methods. Routine inspection for wall thinning is essential for monitoring the structural integrity of these assets and preventing serious accidents. Given the challenges of manned access to these assets, Unmanned Aerial Vehicles (UAVs) equipped with high-resolution cameras are increasingly being adopted as a safer and more efficient alternative for remote inspections. However, their inability to detect sub-surface defects or assess thickness under coatings restricts their applicability. Pulsed Eddy Current (PEC) technology provides a promising solution, capable of assessing thickness beneath coatings and addressing the shortcomings of camera-based inspections. Traditional PEC systems are effective but bulky and difficult to incorporate within mobile platforms, limiting their versatility and ease of deplorability. This paper presents a novel, compact PEC sensor system to address these challenges, enhancing PEC inspections for mobile platforms. The system can be effectively mounted on a crawler-hybrid UAV, facilitating detailed 360-degree inspections of pipe surfaces. Findings detail the autonomous deployment of this PEC system via a UAV for the non-intrusive assessment of wall thickness. Finite element analysis was used for the design and performance evaluation of the PEC system. Integrated with a multirotor-crawler UAV engineered for navigating through complex pipeline environments, this mobile PEC system can conduct thorough evaluations of steel pipeline wall thinning. The system delivers a sensing method that achieves accurate thickness measurements, with errors under 4.8%, facilitating reliable and comprehensive asset inspections.

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