An intelligent manufacturing process diagnosis system using hybrid data mining

  • Authors:

  • Joon Hur;Hongchul Lee;Jun-Geol Baek

  • Affiliations:

  • Department of Industrial Systems and Information Engineering, Korea University, Seoul, Republic of Korea;Department of Industrial Systems and Information Engineering, Korea University, Seoul, Republic of Korea;Department of Industrial Systems Engineering, Induk Institute of Technology, Seoul, Republic of Korea

  • Venue:
  • ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
  • Year:
  • 2006

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Abstract

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).