By Topic

Data Mining for Building Rule-based Fault Diagnosis Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Dianhui Wang ; Department of Computer Science and Computer Engineering, LaTrobe University, Melbourne, VIC3086, Australia. E-mail: dh.wang@latrobe.edu.au

This paper aims at developing rule-based fault diagnosis (RBFD) systems using data mining techniques, where we address a problem of generating rules for faults with low probability of occurrence but considerable conceptual importance. Main technical contributions include a multilayer structure of rule generation and use, and a regularization model embedding some information on recognition rate, coverage rate and generalization capability for rule optimization. A case study is carried out by an engine diagnostics to illustrate effectiveness of our methodology.

Published in:

2006 Chinese Control Conference

Date of Conference:

7-11 Aug. 2006