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Emissivity Correction and Thermal Pattern Reconstruction in Eddy Current Pulsed Thermography

Lookup NU author(s): Kong Jing Li, Professor Gui Yun TianORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2023 by the authors. Emissivity variations are one of the most critical challenges in thermography technologies; this is due to the temperature calculation strongly depending on emissivity settings for infrared signal extraction and evaluation. This paper describes an emissivity correction and thermal pattern reconstruction technique based on physical process modelling and thermal feature extraction, for eddy current pulsed thermography. An emissivity correction algorithm is proposed to address the pattern observation issues of thermography in both spatial and time domains. The main novelty of this method is that the thermal pattern can be corrected based on the averaged normalization of thermal features. In practice, the proposed method brings benefits in enhancing the detectability of the faults and characterization of the materials without the interference of the emissivity variation problem at the object’s surfaces. The proposed technique is verified in several experimental studies, such as the case-depth evaluation of heat-treatment steels, failures, and fatigues of gears made of the heat-treated steels that are used for rolling stock applications. The proposed technique can improve the detectability of the thermography-based inspection methods and would improve the inspection efficiency for high-speed NDT&E applications, such as rolling stock applications.

Publication metadata

Author(s): Li K, Tian GY, Ahmed J

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2023

Volume: 23

Issue: 5

Print publication date: 01/03/2023

Online publication date: 28/02/2023

Acceptance date: 08/02/2023

Date deposited: 29/03/2023

ISSN (print): 1424-8239

ISSN (electronic): 1424-8220

Publisher: MDPI AG


DOI: 10.3390/s23052646

PubMed id: 36904849


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Funder referenceFunder name
NSFC jointed project Intelligent Sensing & Monitoring of Running Gears, grant number 61960206010