The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
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A Machine Learning Based Facility Error Pattern Extraction Framework for Smart Manufacturing

스마트제조를 위한 머신러닝 기반의 설비 오류 발생 패턴 도출 프레임워크

  • Yun, Joonseo (Department of Industrial and Management Engineering, Kyonggi University) ;
  • An, Hyeontae (Department of Industrial and Management Engineering, Kyonggi University) ;
  • Choi, Yerim (Department of Industrial and Management Engineering, Kyonggi University)
  • Received : 2018.04.10
  • Accepted : 2018.05.21
  • Published : 2018.05.31
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Abstract

With the advent of the 4-th industrial revolution, manufacturing companies have increasing interests in the realization of smart manufacturing by utilizing their accumulated facilities data. However, most previous research dealt with the structured data such as sensor signals, and only a little focused on the unstructured data such as text, which actually comprises a large portion of the accumulated data. Therefore, we propose an association rule mining based facility error pattern extraction framework, where text data written by operators are analyzed. Specifically, phrases were extracted and utilized as a unit for text data analysis since a word, which normally used as a unit for text data analysis, is unable to deliver the technical meanings of facility errors. Performances of the proposed framework were evaluated by addressing a real-world case, and it is expected that the productivity of manufacturing companies will be enhanced by adopting the proposed framework.

4차 산업혁명 시대를 맞아, 제조 기업들은 생산성 향상을 위해 축적된 설비 데이터를 활용하여 스마트제조를 실현하는 것에 높은 관심을 두고 있다. 하지만 기존의 설비 데이터 분석 연구들은 주로 센서 데이터 등 정형 데이터를 대상으로 하여, 실제 큰 비중을 차지하고 있는 텍스트와 같은 비정형 데이터에 대한 분석 연구는 부족한 실정이다. 특히, 작업자가 수기로 작성한 텍스트 데이터를 활용한 사례는 매우 적었다. 따라서 본 논문에서는 작업자가 수기로 작성한 설비 오류 데이터를 분석하여 연관 규칙 마이닝을 통해 설비 오류 발생 패턴을 도출하는 프레임워크를 제안하고자 한다. 이때, 일반적인 텍스트 분석 기법과 같이 단어를 분석 기준으로 사용하는 경우 전문 용어에 해당하는 설비 오류의 의미를 표현하는 데에 한계가 있다는 점에 착안하여 구절을 추출하여 텍스트 분석 기준으로 사용하였다. 제안하는 프레임워크의 성능을 실제 사례를 통해 검증하였으며, 본 연구 결과를 활용하면 설비 오류를 예방하여 가동률을 높이고 나아가 제조 기업의 생산성 향상에 기여할 수 있을 것으로 기대한다.

Keywords

References

  1. Bae, S. M., Lee, H. W., Lee, G. A., Choi, S., and Park, H. K., "Enhancing Manufacturing Data Quality for Data Mining, Korean Society for Precision Engineering," In Proceedings of the Korean Society of Precision Engineering Conference, pp. 795-796, 2007.
  2. Baek, D. H. and Han, C. H., "Application of Data Mining for Improving and Predicting Yield in Wafer Fabrication System," Journal of Korea Intelligent Information Systems Society, Vol. 9, No. 1, pp. 157-177, 2003.
  3. Byeon, S. K., Kang, C. W., and Sim, S. B., "Defect Type Prediction Method in Manufacturing Process Using Data Mining Technique," Journal of the Society of Korea Industrial and Systems Engineering, Vol. 27, No. 2, pp. 10-16, 2004.
  4. Han, J., Pei, J., and Yin, Y., "Mining Frequent Patterns Without Candidate Generation," ACM Sigmod Record, Vol. 29, No. 2, pp. 1-12, 2000. https://doi.org/10.1145/335191.335372
  5. Jacobs, A., "The Pathologies of Big Data," Communications of the Association for Computing Machinery, Vol. 52, No. 8, pp. 36-44, 2009.
  6. Jin, H. J., Kim, H. T., and Lee, Y. H., "A Context-Aware System for Reliable RFID-Based Logistics Management," Journal of Society for e-Business Studies, Vol. 18, No. 2, pp. 223-240, 2013. https://doi.org/10.7838/jsebs.2013.18.2.223
  7. Kang, S. K. and Kim, B. T., Preface, The Journal of The Korean Institute of Communication Sciences, Vol. 35, No. 1, pp. 4-4, 2017.
  8. Kim, D. S., Wong, W. X. S., Lim, M. S., Liu, C., Kim, N. G., Park, J. Y., Kil, W. Y., and Yoon, H. S., "A Methodology for Analyzing Public Opinion about Science and Technology Issues Using Text Analysis," Journal of Information Technology Services, Vol. 14, No. 3, pp. 33-48, 2015.
  9. Kim, D. S. and Kim, N. G., "Mapping Categories of Heterogeneous Sources Using Text Analytics," Journal of Intelligent Information Systems, Vol. 22, No. 4, pp. 193-215, 2016. https://doi.org/10.13088/jiis.2016.22.4.193
  10. Kim, K. Y. and Bae, S. J., "Establishing Method of RAM Objective Considering Combat Readiness and Field Data of Similarity Equipment," Journal of the Society of Korea Industrial and Systems Engineering, Vol. 32, No. 3, pp. 127-134, 2009.
  11. Kim, N. G., Lee, D. H., Choi, H. C., and Wong, W. X. S., "Investigations on Techniques and Applications of Text Analytics," The Journal of Korean Institute of Communications and Information Sciences, Vol. 42, No. 2, pp. 471-492, 2017. https://doi.org/10.7840/kics.2017.42.2.471
  12. Kim, S. R., Jang, G. S., and Cho, C. W., "Case Study of Design and Implementation for Hadoop-Based Integrated Facility Monitoring System," Journal of Korean Institute of Industrial Engineers, Vol. 40, No. 1, pp. 34-42, 2014. https://doi.org/10.7232/JKIIE.2014.40.1.034
  13. Kim, S. Y. and Song, M. K., "Application of MI-NPS Digital Factory Methodology for Production Ability Improvement and Optimal Layout Design: Applied Case to Vehicle Shaft Manufacturing Line," Korea Productivity Association, Vol. 28, No. 1, pp. 47-73, 2014.
  14. KTPM Management Consulting, Failure Zero Propulsion Manual, 2001.
  15. Douglas, L., 3D Data Management: Controlling Data Volume, Velocity, and Variety, META Group, 2001.
  16. Lee, I. M., Yoo, S. B., Joung, H. S., and No, S. H., "Study on Power Equipment Maintenance and Replacement Cycle," In Proceedings of the Korean Institute of Electrical Engineers, pp. 1460-1460, 2015.
  17. Lee, D. H., Park, J. H., and Bae, H. R., "Comparison Between Planned and Actual Data of Block Assembly Process Using Process Mining in Shipyards," Journal of Society for e-Business Studies, Vol. 18, No. 4, pp. 145-167, 2013. https://doi.org/10.7838/jsebs.2013.18.4.145
  18. Lee, S. W. and Lee, J. K., "Equipment Management Through Equipment Direct Interface," In Proceedings of Academic Conference Symposium, pp. 523-524, 2015.
  19. Lee, W. B. and Kim, S. K., "An Empirical Research on the Impact of Growth in a National Industrial Complex on Job Creation," Journal of The Korean Regional Development Association, Vol. 26, No. 2, pp. 79-100, 2014.
  20. Lim, M. S. and Kim, N. G., "Investigating Dynamic Mutation Process of Issues Using Unstructured Text Analysis," Journal of Intelligent Information Systems, Vol. 22, No. 1, pp. 1-18, 2016.
  21. Lim, J. W., Jo, D. H., Lee, S. Y., Park, H. J., and Park, J. W., "A Case Study for the Smart Factory Application in the Manufacturing Industry," Korean Journal of Business Administration, Vol. 30, No. 9, pp. 1609-1630, 2017. https://doi.org/10.18032/kaaba.2017.30.9.1609
  22. Noh, K. S. and Park, S. H., "An Exploratory Study on Application Plan of Big Data to Manufacturing Execution System," Journal of Digital Convergence, Vol. 12, No. 1, pp. 305-311, 2014. https://doi.org/10.14400/JDPM.2014.12.1.305
  23. Pak, H. W., "Trends in Production and Manufacturing Technologies Related to Smart Factories," Korea Institute of Communication Sciences, Vol. 33, No. 1, pp. 24-29, 2015.
  24. Park, C. G., Roh, H. S., Choi, Y. J., Kim, H. W., and Lee, J. K., "A Study on the Application Methods of Big Data in the Technology Commercialization Process," Journal of Society for e-Business Studies, Vol. 19, No. 4, pp. 73-99, 2014. https://doi.org/10.7838/jsebs.2014.19.4.073
  25. Park, E. J. and Byung-Hyun, H. A., "A Formal Framework for Analyzing Performance of Container Terminal Operations," Journal of Society for e-Business Studies, Vol. 18, No. 2, pp. 191-203, 2013. https://doi.org/10.7838/jsebs.2013.18.2.191
  26. Park, J. B. and Bae, Y. J., "A Study on Management System for Improving of Equipment Productivity in Injection Machining Process," Journal of the Korea Safety Management & Science, Vol. 19, No. 1, pp. 219-226, 2017. https://doi.org/10.12812/ksms.2017.19.1.219
  27. Schwab, K., The Fourth Industrial Revolution, Crown Business, 2017.
  28. Seo, M. K. and Yun, W. Y., "Clustering-Based Monitoring and Fault Detection in Hot Strip Roughing Mill," Journal of the Korean Society for Quality Management, Vol. 45, No. 1, pp. 25-38, 2017. https://doi.org/10.7469/JKSQM.2017.45.1.025
  29. Taner, T. and Sivrioglu, M., "Energy-Exergy Analysis and Optimization of a Model Sugar Factory in Turkey," Energy, Vol. 93, No. 1, pp. 641-654, 2015. https://doi.org/10.1016/j.energy.2015.09.007
  30. Kabitzsch, K., Vasyutynskyy, V., and Kotte, G., "Troubleshooting in MES of Semiconductor Factory," In Proceedings of the International Conference on Production Research, 2005.
  31. Yun, Y. G. and Gang, Y. S., "A Study on Estimation Flow of Information Analysis for Prevention of Human Error to the Operation," In Proceedings of the Safety Management and Science Conference, pp. 231-241, 2013.