An intelligent manufacturing process diagnosis system using hybrid data mining

Research output: Contribution to journalConference articlepeer-review

8 Citations (Scopus)


The high cost of maintaining a complex manufacturing process necessitates the enhancement of an efficient maintenance system. For the efficient maintenance of manufacturing process, precise diagnosis of the manufacturing process should be performed and the appropriate maintenance action should be executed when the current condition of the manufacturing system is diagnosed as being in abnormal condition. This paper suggests an intelligent manufacturing process diagnosis system using hybrid data mining. In this system, the cause-and-effect rules for the manufacturing process condition are inferred by hybrid decision tree/evolution strategies learning and the most effective maintenance action is recommended by a decision network and AHP (analytical hierarchy process). To verify the hybrid learning proposed in this paper, we compared the accuracy of the hybrid learning with that of the general decision tree learning algorithm (C4.5) and hybrid decision tree/genetic algorithm learning by using datasets from the well-known dataset repository at UCI (University of California at Irvine).

Original languageEnglish
Pages (from-to)561-575
Number of pages15
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4065 LNAI
Publication statusPublished - 2006
Event6th Industrial Conference on Data Mining, ICDM 2006 - Leipzig, Germany
Duration: 2006 Jul 142006 Jul 15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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