Classification of eddy current signals using fuzzy logic and neural networks

Hartmut Ewald; Michael Stieper

doi:10.1117/12.259171

14 November 1996 Classification of eddy current signals using fuzzy logic and neural networks

Hartmut Ewald, Michael Stieper

Proceedings Volume 2947, Nondestructive Evaluation of Utilities and Pipelines; (1996) https://doi.org/10.1117/12.259171
Event: Nondestructive Evaluation Techniques for Aging Infrastructure and Manufacturing, 1996, Scottsdale, AZ, United States

Abstract

The nondestructive eddy current methods are commonly used for automated defect inspection to detect cracks in materials which are used in cars, power and aircraft industries. The eddy current signal from a infinitely long crack can be classified with the help of the fuzzy logic and the neural network techniques. A rule based fuzzy logic classification guarantees better results than fuzzy-cluster- means algorithm, because the classification results can be increased in this case step by step. By using the neural network for the classification of the crack signals it is very important to have a good 'learning pattern.' The advantage of time-delay networks in this application is the fact that the network can 'learn' the eddy-current time signal; a signal preprocessing is not necessary.

Citation Download Citation

Hartmut Ewald and Michael Stieper "Classification of eddy current signals using fuzzy logic and neural networks", Proc. SPIE 2947, Nondestructive Evaluation of Utilities and Pipelines, (14 November 1996); https://doi.org/10.1117/12.259171

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available