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Lookup NU author(s): Emeritus Professor Gui Yun Tian
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Microscale or early damage often occurs on a variable-curvature railhead profile, impacting railway safety and maintenance. However, quantitatively nondestructive testing (NDT) microdamage on variable-curvature railhead profile, such as submillimeter cracks, remains challenging due to the limited spatial resolution of electromagnetic probes and material inhomogeneity. Moreover, as service-deformed railheads accumulate progressively variable curvature during service, rigid probes’ poor contact with them induces a lift-off effect, degrading signal-to-noise ratio (SNR) and increasing microdamage misses. To overcome this limitation, a novel high-resolution flexible magnetic Barkhausen noise (MBN) sensor is designed and instrumented for submillimeter crack detection on a variable-curvature railhead profile. In particular, the sensitivity of the flexible MBN sensing for a variety of submillimeter cracks is optimized by analyzing the relationship mechanism of the magnetic flux variation generated by the magnetic excitation yoke and the microvoltage captured by the optimized flexible induction coil. The eigenvalues of the MBN signal are extracted for quantitative evaluation of the microdamage in the railhead profile. The simulation and experimental results demonstrate that the flexible MBN sensor exhibits higher sensitivity, repeatability, and SNR than the rigid MBN and the commercial MBN sensing in detecting submillimeter-width cracks with micromiter-to-millimeter depths on the railhead profile. Accordingly, the proposed flexible MBN probe can be expected to detect the submillimeter crack and microstructural evolution in variable-curvature components, including rail wheels and pipelines.
Author(s): Liu J, Sun Z, Wang X, Wang W, Chen C, Tian G
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Instrumentation and Measurement
Year: 2026
Volume: 75
Online publication date: 13/07/2026
Acceptance date: 27/06/2026
ISSN (print): 0018-9456
ISSN (electronic): 1557-9662
Publisher: IEEE
URL: https://doi.org/10.1109/TIM.2026.3712973
DOI: 10.1109/TIM.2026.3712973
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