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Data Mining for Building Rule-based Fault Diagnosis Systems

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

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