Wavelet aided multivariate outlier analysis to enhance defect contrast in thermal images

Arun Manohar; Francesco Lanza di Scalea

doi:10.1117/12.880173

13 April 2011 Wavelet aided multivariate outlier analysis to enhance defect contrast in thermal images

Arun Manohar, Francesco Lanza di Scalea

Proceedings Volume 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011; 79810V (2011) https://doi.org/10.1117/12.880173
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2011, San Diego, California, United States

Abstract

A novel two-stage signal reconstruction approach is proposed to analyze raw thermal image sequences for damage detection purposes by Infrared Thermographic NDE. The first stage involves low-pass filtering using Wavelets. In the second stage, a Multivariate Outlier Analysis is performed on filtered data using a set of signal features. The proposed approach significantly enhances the defective area contrast against the background in infrared thermography NDE. The two-stage approach has some advantages in comparison to the traditionally used methods, including automation in the defect detection process and better defective area isolation through increased contrast. The method does not require a reference area to function. The results are presented for the case of a composite plate with simulated delaminations, and a composite sandwich plate with skin-core disbonds.

Citation Download Citation

Arun Manohar and Francesco Lanza di Scalea "Wavelet aided multivariate outlier analysis to enhance defect contrast in thermal images", Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 79810V (13 April 2011); https://doi.org/10.1117/12.880173

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