1. Thermal resistance field estimations from IR thermography using multiscale Bayesian inference
- Author
-
Alain Sommier, S. Chevalier, Marie-Marthe Groz, E. Abisset, Jean-Luc Battaglia, Christophe Pradere, Jean-Christophe Batsale, Institut de Mécanique et d'Ingénierie (I2M), Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Arts et Métiers Sciences et Technologies, and HESAM Université (HESAM)-HESAM Université (HESAM)
- Subjects
010302 applied physics ,Resistive touchscreen ,Materials science ,Field (physics) ,Infrared ,Thermal resistance ,InfraRed Thermography ,02 engineering and technology ,Inverse Processing ,Quantitative Thermal Resistance Fields Estimation ,021001 nanoscience & nanotechnology ,Bayesian inference ,01 natural sciences ,[SPI.MAT]Engineering Sciences [physics]/Materials ,Flash (photography) ,0103 physical sciences ,Thermography ,Thermal ,Bayesian Inference ,[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det] ,Electrical and Electronic Engineering ,0210 nano-technology ,Instrumentation ,Remote sensing - Abstract
International audience; The main goal of this paper is the estimation of thermal resistive fields in multilayer samples using the classical front face flash method as excitation and InfRared Thermography (IRT) as a monitoring sensor. The complete inverse processing of a multilayer analytical model can be difficult due to a lack of sensitivity to certain parameters (layer thickness, depth of thermal resistance, etc.) or processing time. For these reasons, our present strategy proposes a Bayesian inference approach. Using the analytical quadrupole method, a reference model can be calculated for a set of parameters. Then, the Bayesian probabilistic method is used to determine the maximum likelihood probability between the measured data and the reference model. To keep the processing method robust and fast, an automatic selection of the calculation range is proposed. Finally, in the case of a bilayer sample, both the thickness and resistive 3D layers are estimated in less than 2 min for a space and time matrix of 50,000 pixels by 4000 time steps with a reasonable relative error of less than 5%.
- Published
- 2020