An Intelligent Manufacturing Process Diagnosis System Using Hybrid Data Mining

  • Joon Hur
  • Hongchul Lee
  • Jun-Geol Baek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)


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).


Analytic Hierarchy Process Leaf Node Maintenance Action Normal Node Abnormal Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Joon Hur
    • 1
  • Hongchul Lee
    • 1
  • Jun-Geol Baek
    • 2
  1. 1.Department of Industrial Systems and Information EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Department of Industrial Systems EngineeringInduk Institute of TechnologySeoulRepublic of Korea

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