Infrared Non-Destructive Testing via Semi-Nonnegative Matrix Factorization

Bardia Yousefi,Clemente Ibarra-Castanedo,Xavier P.V Maldague

doi:10.3390/proceedings2019027013

Bardia Yousefi, Clemente Ibarra-Castanedo + Show 1 more

Open Access

https://doi.org/10.3390/proceedings2019027013

Copy DOI

Publication Date: Sep 20, 2019
Citations: 4	License type: CC BY 4.0

Affiliation: Université Laval

Abstract

Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.

Highlights

A matrix decomposition processed by Non-negative matrix factorization (NMF) leads to decomposing an input matrix into two low-rank non-negative matrices similar to principal component analysis (PCA) but with non-negative matrix constraints [1]
Matrix factorization has been used for infrared non-destructive testing (IR-NDT)
Compared to PCA and archetypal analysis (AA) to assess its advantages and pitfalls as reported in [5]. This analysis continued in [6] where NMF was applied to cultural heritage objects and buildings using gradient descend (GD) and non-negative least square (NNLS) and demonstrating the good performance of such algorithms for detecting subsurface defects

Summary

Introduction

A matrix decomposition processed by Non-negative matrix factorization (NMF) leads to decomposing an input matrix into two low-rank non-negative matrices similar to principal component analysis (PCA) but with non-negative matrix constraints [1]. Compared to PCA and archetypal analysis (AA) to assess its advantages and pitfalls as reported in [5]. This analysis continued in [6] where NMF was applied to cultural heritage objects and buildings using gradient descend (GD) and non-negative least square (NNLS) and demonstrating the good performance of such algorithms for detecting subsurface defects. We present Semi-NMF using NNLS and based on gradient descent rule (Ruls) methods to detect subsurface defects in Aluminum.

Testing Specimen and Experimental Setup

Methodology

Results and Conclusions

Method