Location

Ithaca, NY

Start Date

1978 12:00 AM

Description

The ultimate resolution of all ultrasonic flaw detection systems is limited by transducer response. Although the system output contains detailed information about the target structure, these details are masked by the system characteristics. Since the output can be described as the convolution of the target response and the impulse response of the system, it should- in principle - be possible to reverse this operation and extract the target response. In practice, it is found that the presence of even relatively small amounts of noise make the deconvolution process impossible. If, however, the flaw detection system has an extremely high output signal-to-noise ratio it is possible to use estimation techniques in the deconvolution process to achieve a good approximation to the actual target response. Results are presented that demonstrate these techniques applied to both simulated and experimental data. Coupling deconvolution processing with feature extraction is shown to yield an order of magnitude increase in range resolution.

Book Title

Proceedings of the ARPA/AFML Review of Progress in Quantitative NDE

Chapter

8. New Technology Roots

Pages

312-318

Language

en

File Format

application/pdf

Share

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Jan 1st, 12:00 AM

Deconvolution Processing for Flaw Signatures

Ithaca, NY

The ultimate resolution of all ultrasonic flaw detection systems is limited by transducer response. Although the system output contains detailed information about the target structure, these details are masked by the system characteristics. Since the output can be described as the convolution of the target response and the impulse response of the system, it should- in principle - be possible to reverse this operation and extract the target response. In practice, it is found that the presence of even relatively small amounts of noise make the deconvolution process impossible. If, however, the flaw detection system has an extremely high output signal-to-noise ratio it is possible to use estimation techniques in the deconvolution process to achieve a good approximation to the actual target response. Results are presented that demonstrate these techniques applied to both simulated and experimental data. Coupling deconvolution processing with feature extraction is shown to yield an order of magnitude increase in range resolution.