Location

Brunswick, ME

Start Date

1-1-1990 12:00 AM

Description

The inverse problem in nondestructiye evaluation involves the characterization of flaw parameters given a transducer response signal. In general the governing equations and boundary conditions describing the underlying physical phenomena are complex. Consequently analytical closed form solutions can be obtained only under. strong simplifying assumptions with regard to geometry and linearity of the problem. This precludes their use as direct inverse models for solving realistic NDT problems necessitating the need for using indirect inverse models based on pattern recognition algorithms. These inverse models classify the NDT signal as belonging to one of the classes of defects stored in a data bank as shown in Fig. 1.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

9A

Chapter

Chapter 3: Interpretive Signal and Image Processing

Section

A: Signal Processing and Neural Networks

Pages

673-680

DOI

10.1007/978-1-4684-5772-8_84

Language

en

File Format

application/pdf

Share

COinS
 
Jan 1st, 12:00 AM

Application of Neural Networks for Classification of Eddy Current NDT Data

Brunswick, ME

The inverse problem in nondestructiye evaluation involves the characterization of flaw parameters given a transducer response signal. In general the governing equations and boundary conditions describing the underlying physical phenomena are complex. Consequently analytical closed form solutions can be obtained only under. strong simplifying assumptions with regard to geometry and linearity of the problem. This precludes their use as direct inverse models for solving realistic NDT problems necessitating the need for using indirect inverse models based on pattern recognition algorithms. These inverse models classify the NDT signal as belonging to one of the classes of defects stored in a data bank as shown in Fig. 1.