Use of artificial intelligence techniques to fault diagnosis in analog systems

Authors:
Jerzy Rutkowski;Damian Grzechca
Affiliations:
Department of Automatic Control, Electronics, and Computer Science, Silesian University of Technology, Gliwice, Poland;Department of Automatic Control, Electronics, and Computer Science, Silesian University of Technology, Gliwice, Poland
Venue:
ECC'08 Proceedings of the 2nd conference on European computing conference
Year:
2008

Citing 3
Cited 3

Analog Testing with Time Response Parameters

IEEE Design & Test
The Influence of Global Parametric Faults on Analogue Electronic Circuits Time Domain Response Features

DDECS '08 Proceedings of the 2008 11th IEEE Workshop on Design and Diagnostics of Electronic Circuits and Systems
Applying a robust heteroscedastic probabilistic neural network to analog fault detection and classification

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Intelligent control system for monitoring drilling process

CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Computer added monitoring of drilling rig systems

ECC'09 Proceedings of the 3rd international conference on European computing conference
Program recursive forms and programming automatization for functional languages

WSEAS Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

Basic concepts of fault diagnosis in analog and mixed (analog and digital) electronic systems by means of the simulation-before-test approach, the so called dictionary approach, have been presented. Special attention has been paid to application of artificial intelligence tools, such as: artificial neural networks, fuzzy sets and evolutionary computing.