Efficient algorithm for thermal nondestructive testing and evaluation by considering the heteroscedastic nature of noise sources in infrared thermography

Thermal Imaging is a promising Non Destructive Testing & Evaluation ( NDT & E) approach to monitor the health of composite materials. Among various post processing approaches adopted in thermal imaging for NDT & E, statistical analysis schemes gained importance due to their reliability and data reduction capabilities. This paper provides an insight to a factor analysis-based statistical approach to detect the hidden defects in the Glass Fiber Reinforced Polymer ( GFRP ) sample. The proposed approach models the observed data covariance into combination of temporal signal covariance and noise covariance matrices. The modeling of the diagonal covariance matrix ( with different elements) is motivated by the presence of heterogeneity in the experimental data obtained from GFRP sample.This novel method is based on the coordinate descent technique, which estimates the covariance matrix of the noise variances iteratively by minimizing the negative log likelihood function. The obtained results from the chosen GFRP samples compared with the widely used statistical Principal Component Thermography ( PCT ) technique illustrate the improved performance in terms of defect detection with the proposed technique.