Event Title

Crack Angle and Depth Estimation Using Wavelet Preprocessed Neural Network

Location

Snowbird, UT, USA

Start Date

1-1-1999 12:00 AM

Description

A crack characterization problem must involve analysis of a signal received by appropriate transducer to evaluate the shape and size of crack in test object. The presence of a crack in ferromagnetic tube or plate, tested by magnetic field leakage (MFL) inspection method, causes a redistribution of excited magnetic flux. A neural network (NN) approac (with analog output neurons) can be used for surface-breaking crack depth and angle oblique estimation using signal profile of leakage field.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

18A

Chapter

Chapter 3: Simulations, Signal Processing, Tomography, and Holography

Section

Classification Techniques

Pages

821-828

DOI

10.1007/978-1-4615-4791-4_105

Language

en

File Format

application/pdf

This document is currently not available here.

Share

COinS
 
Jan 1st, 12:00 AM

Crack Angle and Depth Estimation Using Wavelet Preprocessed Neural Network

Snowbird, UT, USA

A crack characterization problem must involve analysis of a signal received by appropriate transducer to evaluate the shape and size of crack in test object. The presence of a crack in ferromagnetic tube or plate, tested by magnetic field leakage (MFL) inspection method, causes a redistribution of excited magnetic flux. A neural network (NN) approac (with analog output neurons) can be used for surface-breaking crack depth and angle oblique estimation using signal profile of leakage field.