Profile detection and alignment technique based on electromagnetic array sensor for rail track inspection systems

Abstract

To ensure the integrity of railway infrastructure and thus the safety and efficiency of rail transportation, rail tracks are regularly inspected for fatigue cracks. The crack detection system must remain centralized relative to the rail head for effective operation. Currently, this is achieved using contact eddy current sensors and non-contact optical methods. Due to the commercial sensitivity of these systems, there are limited reports and scarce technical details available about them. Addressing this challenge, this thesis proposes a non-contact profile detection and alignment technique based on an electromagnetic array sensor for rail track inspection systems. The research implements a sensor array that interfaces with the multichannel system developed in the research lab and conducts a series of detailed experiments to understand the effects of key variables, such as coil configuration, particularly its gap and lift-off distance. Signal quality, measured in terms of signal-to-noise ratio (SNR), is evaluated for all scenarios. Furthermore, robust and effective algorithms are proposed to locate the center of the rail track. The experiments reveal that increasing the coil distance reduces the SNR to some extent; however, all investigated coil configurations provide excellent indications of the rail center, with an average absolute error of 0.37 mm in peak positioning. In summary, this thesis introduces a novel working technique that effectively detects the rail profile for alignment purposes. It lays the foundation for future innovations in railway inspection technologies and is expected to significantly influence the development of more sophisticated, automated railway maintenance systems. If adopted by the industry, this technique has the potential to make a significant impact on research in this area and the rail inspection field, contributing to safer and more reliable railway operations.

Date of Award	21 Nov 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Anthony Peyton (Co Supervisor) & Wuliang Yin (Main Supervisor)