Location

Snowmass Village, CO

Start Date

1-1-1995 12:00 AM

Description

Flaw depth estimation is crucial in eddy current tubing inspection in order to prevent leak accidents in various types of heat exchangers. Udpa proposed a novel method using neural network to classify four different types of flaws detected by eddy current tubing inspection [1, 2]. They used as the neural network input the Fourier descriptor coefficients of cumulative angular function of flaw signal pattern curve [3]. Their classification is based on the shape differences in signal patterns because the coefficients are invariant under rotation, translation, and scaling of the signal pattern.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

14A

Chapter

Chapter 3: Interpretive Signal Processing and Image Analysis

Section

Neural Nets

Pages

811-818

DOI

10.1007/978-1-4615-1987-4_101

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Flaw Depth Classification in Eddy Current Tubing Inspection by Using neural Network

Snowmass Village, CO

Flaw depth estimation is crucial in eddy current tubing inspection in order to prevent leak accidents in various types of heat exchangers. Udpa proposed a novel method using neural network to classify four different types of flaws detected by eddy current tubing inspection [1, 2]. They used as the neural network input the Fourier descriptor coefficients of cumulative angular function of flaw signal pattern curve [3]. Their classification is based on the shape differences in signal patterns because the coefficients are invariant under rotation, translation, and scaling of the signal pattern.