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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

Robust Principal Component Thermography for Defect Detection in Composites.

Samira Ebrahimi¹, Julien Fleuret¹, Matthieu Klein²

¹Computer Vision and Systems Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This summary is machine-generated.

Robust Principal Component Thermography (RPCT) improves defect detection in Pulsed Thermography (PT) data by using Robust PCA. RPCT enhances defect contrast and segmentation over standard PCA, though it requires more computation time.

Keywords:

CFRP OIALM Orthogonal IALM PCP RPCA Robust PCA noise reduction pulsed thermography

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Area of Science:

Non-destructive testing
Materials science
Signal processing

Background:

Pulsed Thermography (PT) data often suffers from noise, hindering defect detection.
Principal Component Analysis (PCA) is effective for defect enhancement in PT but sensitive to data corruption.
Robust PCA (RPCA) offers an alternative for handling noisy data by separating low-rank and sparse components.

Purpose of the Study:

To introduce Robust Principal Component Thermography (RPCT) for improved defect detection in PT data.
To evaluate RPCT's performance against standard Principal Component Thermography (PCT).
To quantify improvements in defect contrast and segmentation using RPCT.

Main Methods:

Application of RPCA to PT data, termed RPCT.
Comparison of RPCT with PCA-based PCT on CFRP samples with artificial defects.
Quantitative evaluation using Contrast-to-Noise Ratio (CNR) and Jaccard similarity coefficient.

Main Results:

RPCT achieved an average 40% higher CNR compared to PCT.
RPCT showed a slightly higher Jaccard index (0.7395) than PCT (0.7010).
PCT was 11.5 times faster than RPCT in computational time.

Conclusions:

RPCT demonstrates superior defect detectability and segmentation potential in PT compared to PCT.
Further research is needed to optimize RPCT's computational efficiency and test its efficacy on diverse materials.