scholarly journals Addendum: Fang, Q.; Maldague, X. A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning. Appl. Sci. 2020, 10, 6819

2021 ◽  
Vol 11 (8) ◽  
pp. 3451
Author(s):  
Qiang Fang ◽  
Xavier. Maldague
Keyword(s):  
Deep Learning ◽  
Infrared Thermography ◽  
Depth Estimation ◽  
Defect Depth

The authors wish to make the following corrections to this paper [...]

scholarly journals Defect Depth Estimation in Infrared Thermography with Deep Learning

2021 ◽  
Author(s):  
Qiang Fang ◽  
farima abdollahi-mamoudan ◽  
Xavier Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Pulsed Thermography ◽  
And Performance ◽  
Gated Recurrent Units ◽  
Non Destructive

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

scholarly journals A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6819 ◽  
Author(s):  
Qiang Fang ◽  
Xavier Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Pulsed Thermography ◽  
And Performance ◽  
Gated Recurrent Units ◽  
Non Destructive

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRUs) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

scholarly journals Defect Depth Estimation in Infrared Thermography with Deep Learning

2020 ◽  
Author(s):  
Qiang Fang ◽  
and Xavier. Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Reinforced Polymer ◽  
And Performance ◽  
Gated Recurrent Units ◽  
Non Destructive

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

scholarly journals Defect Depth Estimation in Infrared Thermography with Deep Learning

2020 ◽  
Author(s):  
Qiang Fang ◽  
Xavier Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Fiber Reinforced Polymer ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Reinforced Polymer ◽  
And Performance ◽  
Non Destructive

Infrared thermography has already been proved to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Unites (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer (CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantify the depth of defects presented in the Plexiglasses materials. The proposed evaluated the accuracy and performance of synthetic plexiglasses data from FEM for defect depth predictions.

scholarly journals Building Façade Protection Using Spatial and Temporal Deep Learning Models Applied to Thermographic Data. Laboratory Tests

2021 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Iván Garrido ◽  
Eva Barreira ◽  
Ricardo M. S. F. Almeida ◽  
Susana Lagüela
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Laboratory Tests ◽  
Depth Estimation ◽  
Mean Square ◽  
Learning Models ◽  
Defect Depth ◽  
Defect Identification ◽  
Building Facades ◽  
Gated Recurrent Unit

This paper proposes a methodology that combines spatial and temporal deep learning (DL) models applied to data acquired by InfraRed Thermography (IRT). The data were acquired from laboratory specimens that simulate building façades. The spatial DL model (Mask Region-Convolution Neural Network, Mask R-CNN) is used to identify and classify different artificial subsurface defects, whereas the temporal DL model (Gated Recurrent Unit, GRU) is utilized to estimate the depth of each defect, all in an autonomous and automated manner. An F-score average of 92.8 ± 5.4% regarding defect identification and classification, and a root-mean-square error equal to 1 mm in the estimation of defect depth equal to 10 mm as the best defect depth estimation, are obtained with this first application of a combination of spatial and temporal DL models to the IRT inspection of buildings.

scholarly journals A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6387
Author(s):  
Li Xu ◽  
Jianzhong Hu
Keyword(s):  
Infrared Thermography ◽  
Visual Information ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Principal Component ◽  
Defect Depth ◽  
Model Learning ◽  
Flat Bottom ◽  
Bp Network ◽  
Active Infrared Thermography

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

Sign in / Sign up

Export Citation Format

Share Document