Orientation Effect Analysis and Novel Sensor Designs for Eddy-Current Testing Techniques

Abstract

Abstract Efficient and reliable inspection and monitoring of the quality and structural integrity of critical components in equipment is essential for preventing safety accidents, minimizing economic losses, and ensuring personnel safety. Eddy current testing (ECT) is widely employed for detecting surface and subsurface cracks in conductive materials. One of its primary applications is the detection of cracks on or within railway tracks, allowing for the prevention of safety hazards and accidents without requiring direct contact with the track surface. Over the past decades, significant research has been conducted on non-destructive inspection methods and their enhancements to develop more efficient and accurate approaches for detecting damage in metal materials. Among the various non-destructive testing methods extensively studied are eddy current testing, ultrasonic inspection, visual inspection, alternating current field measurement (ACFM), magnetic flux leakage, radiographic testing, dye penetrant testing, and others. This thesis focuses on eddy current testing due to its advantages, including rapid inspection, high sensitivity to small cracks, minimal preparation requirements, and more. The thesis addresses the challenge of detecting short cracks on the surface and subsurface of metal components oriented in various directions (ranging from 0 degrees to 90 degrees in 15-degree increments). To enhance detection capability, a dual-channel output orthogonal excitation coil sensor is proposed in this study, and its eddy current density distributions were analyzed. The crack detection performance of this newly proposed sensor structure was evaluated through multiple experiments conducted with a constructed sensor prototype. Finally, experimental measurements were carried out, and the results demonstrate that the newly designed sensor performs satisfactorily.

Date of Award	31 Dec 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Anthony Peyton (Supervisor) & Wuliang Yin (Supervisor)