Earticle

초록

영어

The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.

목차

Abstract
 1. Introduction
 2. Attribute Selection
 3. Decision Tree Learning
 4. Maintenance Action
  4.1 Decision Network
  4.2 Selecting Maintenance Action
 5. Conclusion
 6. Reference

키워드

저자

• 허준 [ Hur Joon | Ph. D Candidate, Dept. of Industrial System and Information engineering , Korea University ]
• 백준걸 [ Baek Jun Geol | Professor, Dept. of Industrial System engineering , Induk Institute of Technology ]
• 이홍철 [ Lee Hong Chul | Professor, Dept. of Industrial System and Information engineering, Korea University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620