A data processing algorithm for multispectral radiation thermometry based on multi-segment linear model and secondary inversion

• Published in: Measurement : journal of the International Measurement Confederation Vol. 201; p. 111753
• Main Authors: Zhang, Yucun, Zou, Zhou, Yan, Fang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.09.2022
• Subjects: DFP; Emissivity; Linear model; Multispectral radiation thermometry; Optimization; Multispectral radiation thermometry; Emissivity; Linear model; Optimization; DFP
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.111753

•The optimization problem of multispectral radiation thermometry is constructed based on the multi-segment linear emissivity model, which has a unique least square solution.•According to the median for the value range of emissivity, the appropriate wavelengths used for establishing multi-segment linear emissivity model can be determined.•The problem of channel temperature inconsistency in the hypothetical model method of multispectral radiation thermometry is solved by the secondary inversion. Multispectral radiation thermometry is a branch of infrared temperature measurement technology with wide application in many industries. Although this technology has been developed for decades, there is still a problem in its data processing, that is, how to accurately solve the radiation equations without the information of object’s emissivity. The traditional fixed emissivity model method cannot adapt to all objects. Therefore, this study proposes a data processing algorithm for multispectral radiation thermometry based on multi-segment linear model and secondary inversion. The algorithm can automatically select the multi-segment linear model suitable for the measured object, and realize the accurate calculation of temperature and emissivity through the secondary inversion. The efficiency and accuracy of the algorithm are verified through simulations. The experimental results of temperature measurement of aviation alloy prove the effectiveness of this algorithm in practical application.