Determination of Defect Depth and Size Using Virtual Heat Sources in Pulsed Infrared Thermography

Abstract The simple method of determining thermal diffusivity of solid materials at room temperature using the pulsed infrared thermography (IRT) is proposed. The theoretical basis of the method and experimental results are presented. The study was conducted on austenitic steel 316L. Theobtained results show that the thermal diffusivity value of the tested steel determined by means of pulsed infrared thermography is very approximate to the values given in the literature, obtained by using more complicated methods. The differences between these values are 0.5%.

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Sizing the thermal resistance of vertical cracks using pulsed infrared thermography with laser spot excitation

10.21611/qirt.2016.112 ◽

2016 ◽

Cited By ~ 1

Author(s):

N.W. Pech-May ◽

A. Oleaga ◽

A. Mendioroz ◽

A. Salazar

Keyword(s):

Thermal Resistance ◽

Infrared Thermography ◽

Laser Spot ◽

Pulsed Infrared Thermography

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Debonding defects detection of FMLs based on long pulsed infrared thermography technique

Infrared Physics & Technology ◽

10.1016/j.infrared.2019.103074 ◽

2020 ◽

Vol 104 ◽

pp. 103074 ◽

Cited By ~ 1

Author(s):

Chiwu Bu ◽

Guozeng Liu ◽

Xibin Zhang ◽

Qingju Tang

Keyword(s):

Infrared Thermography ◽

Pulsed Infrared Thermography

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Infrared Thermography Measurement for Vibration-Based Structural Health Monitoring in Low-Visibility Harsh Environments

Sensors ◽

10.3390/s20247067 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7067

Author(s):

Jia-Hao He ◽

Ding-Peng Liu ◽

Cheng-Hsien Chung ◽

Hsin-Haou Huang

Keyword(s):

Structural Health Monitoring ◽

Infrared Thermography ◽

Mode Shape ◽

Health Monitoring ◽

Large Scale ◽

Frame Structure ◽

Harsh Environments ◽

Thermal Imager ◽

Heat Sources ◽

Structural Health

In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures.

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

Applied Sciences ◽

10.3390/app11146387 ◽

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.

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