Toggle Main Menu Toggle Search

Open Access padlockePrints

Uncertainty metric in model-based eddy current inversion using the adaptive Monte Carlo method

Lookup NU author(s): Professor Gui Yun TianORCiD


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 2019 Elsevier Ltd The accuracy of model-based inversion largely depends on the gap between simulated and observed results. However, the gap does not disappear even if a wide range of calibrations or compensations are made. In essence, the gap varies statistically due to random noises and poorly known parameters, causing predicted results to change in a random manner. In this work, an adaptive Monte Carlo method is proposed to evaluate the uncertainty of model-based inversion for eddy current nondestructive evaluation. Using the presented Monte Carlo method, the influences of excitation frequencies on uncertainty metric were identified, and uncertainty metric was calculated when the conductivity, thickness and liftoff distance were inferred for characterization of a plate. The presented Monte Carlo based method allows efficient and cost-effective assessment of uncertainty and could enable us to identify the factors that play a dominant role in the performances of model-based inversion.

Publication metadata

Author(s): Fan M, Wu G, Cao B, Sarkodie-Gyan T, Li Z, Tian G

Publication type: Article

Publication status: Published

Journal: Measurement: Journal of the International Measurement Confederation

Year: 2019

Volume: 137

Pages: 323-331

Print publication date: 01/04/2019

Online publication date: 22/01/2019

Acceptance date: 04/01/2019

ISSN (print): 0263-2241

Publisher: Elsevier B.V.


DOI: 10.1016/j.measurement.2019.01.004


Altmetrics provided by Altmetric