A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images

ABSTRACT

Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants, and oil pipelines to prevent catastrophic events. Many modern NDE systems generate image data. In some applications, an experienced inspector performs the tedious task of visually examining every image to provide accurate conclusions about the existence of flaws. This approach is labor-intensive and can cause misses due to operator ennui. Automated evaluation methods seek to eliminate human-factors variability and improve throughput. Simple methods based on peak amplitude in an image are sometimes employed and a trained-operator-controlled refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR) has also been implemented. We develop an automated and optimized detection procedure that mimics these operations. The primary goal of our methodology is to reduce the number of images requiring expert visual evaluation by filtering out images that are overwhelmingly definitive on the existence or absence of a flaw. We use an appropriate model for the observed values of the SNR-detection criterion to estimate the probability of detection. Our methodology outperforms current methods in terms of its ability to detect flaws. Supplementary materials for this article are available online.

KEYWORDS:

• Dynamic thresholding
• Image processing
• Matched filter
• Noise-interference model
• Probability of detection
• Signal-to-noise ratio

Supplementary Materials

Sections S-1–S-3 along with Figures S-1 and S-2 are in the supplementary materials archive of the journal website (supplement.pdf).

Acknowledgments

This work was performed with partial support from the Federal Aviation Administration under contract number DTFACT-09-C-00006 through the Center for Nondestructive Evaluation at Iowa State University. The ultrasonic test images used in Section 2.2.3 were acquired as part of a project to study the POD of ultrasonic inspections of forgings, supported by the Federal Aviation Administration under contract number 08-C-00005 to Iowa State University. The authors thank Tim Gray for providing the images and for helping to interpret them. The authors also thank the editor, an associate Editor, and reviewers for suggestions that helped improve an earlier version of this article.