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A Study on Fault Prediction Method in a Pump Tower of LNG FPSO

LNG FPSO 펌프타워 고장 예지 방안에 관한 연구

  • Kim, Yongjae (Dept. of Industrial Engineering, Hongik Univ.) ;
  • Cho, SangJe (ICT for Sustainable Manufacturing Laboratory (SCI-STI-DK), EPFL) ;
  • Jun, Hong-Bae (Dept. of Industrial Engineering, Hongik Univ.) ;
  • Ha, Chunghun (Dept. of Industrial Engineering, Hongik Univ.) ;
  • Shin, Jongho (Dept. of Design and Human Engineering, UNIST)
  • 김용재 (홍익대학교 산업공학과) ;
  • 조상제 (스위스 연방 로잔 공대 ICT4SM 연구실) ;
  • 전홍배 (홍익대학교 산업공학과) ;
  • 하정훈 (홍익대학교 산업공학과) ;
  • 신종호 (울산과기대 디자인 및 인간공학과)
  • Received : 2015.10.27
  • Accepted : 2016.01.20
  • Published : 2016.06.01

Abstract

The plant equipment usually has a long life cycle. During its O&M (Operation & Maintenance) phase, since the occurrence of an accident of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Nowadays due to the emerging ICTs (Information Communication Technologies) and sensor technologies, it is possible to gather the health status data of important offshore equipment and their environment data, which leads to much concern on CBM (Condition-Based Maintenance). In this study, we will propose an approach to estimate the remaining lifetime of an offshore plant equipment (pump tower) based on gathered ocean environment data.

Keywords

References

  1. Park, K.S., 2015, Offshore Business, Korea Maritime Institute, 19, pp.7-12.
  2. Cho, S.J. and Jun, H.B., 2014, A Study on the Development of Prognosis System for LNG FPSO Compressor Equipment, Proceedings of the Society of CAD/CAM Engineers Conference, pp.304-309.
  3. Jardine, A.K.S., Lin, D. and Banjevic, D., 2005, A Review on Machinery Diagnostics and Prognostics Implementing Condition-based Maintenance, Mechanical Systems and Signal Processing, 20(7), pp.1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012
  4. Lee, L.D., 2001, Using Wireless Technology and the Internet for Predictive Maintenance, Hydrocarbon Processing, 80, pp.77-96.
  5. Djurdjanovic, D., Lee, J. and Ni, J., 2003, Watchdog Agent-an Infotronics-based Prognostics Approach for Product Performance Degradation Assessment and Prediction, Advanced Engineering Informatics, 17, pp.109-125. https://doi.org/10.1016/j.aei.2004.07.005
  6. Lin, D., Wiseman, M., Banjevic, D. and Jardine, A.K., 2004, An Approach to Signal Processing and Condition-based Maintenance for Gearboxes Subject to Tooth Failure, Mechanical Systems and Signal Processing, 18(5), pp.993-1007. https://doi.org/10.1016/j.ymssp.2003.10.005
  7. Yan, J., Koc, M. and Lee, J., 2004, A Prognostic Algorithm for Machine Performance Assessment and Its Application, Production Planning & Control, 15(8), pp.796-801. https://doi.org/10.1080/09537280412331309208
  8. Bansal, D., Evans, D.J. and Jones, B., 2004, A Real-time Predictive Maintenance System for Machine Systems, International Journal of Machine Tools and Manufacture, 44(7-8), pp.759-766. https://doi.org/10.1016/j.ijmachtools.2004.02.004
  9. Kothamasu, R., Huang, S.H. and Verduin, W.H., 2006, System Health Monitoring and Prognostics-a Review of Current Paradigms and Practices, International Journal of Advanced Manufacturing Technology, 28, pp.1012-1024. https://doi.org/10.1007/s00170-004-2131-6
  10. Jardine, A.K.S., Lin, D. and Banjevic, D., 2006, A Review on Machinery Diagnostics and Prognostics Implementing Condition-based Maintenance, Mechanical Systems and Signal Processing, 20, pp.1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012
  11. Oh, S.G., Lee, S.H. and Lee, M.S., 2012, Facility Maintenance Management System Based on Predictive Maintenance for Manufacturing Industry, Korean Society of Precision Engineering, pp.643-644.
  12. Shin, J.H., Jun, H.B., Cattaneo, C., Kiritsis, D. and Xirouchakis, P., 2013, A Method for Evaluating Product Degradation Status Using Product Usage Data, Transactions of the Society of CAD/CAM Engineers, 18(1), pp.36-48. https://doi.org/10.7315/CADCAM.2013.36
  13. Lee, Y.E., Kang, J.Y., Kwak, J.J., Ahn, K.I. and Cho, M.J., 2014, Study on Reliability Prediction Method Application for Mechanical Parts, Proceedings of Korean Aeronautical and Space Sciences, Republic of Korea, pp.16-19.
  14. Park, S.K., Sim, M.S., Lee, H.Y. and Lee, M.J., 2003, Design of Condition Based Maintenance Expert System Using FFT Algorithm, Korea of Information Science Society, 30(2), pp.514-516.
  15. Park, S.W. and Lee, H.M., 2013, Design of Hull Residual Life Prediction System Considering Corrosion and Coating, Journal of the Society of Naval Architects of Korea, 50(2), pp.104-110. https://doi.org/10.3744/SNAK.2013.50.2.104
  16. Kim, H.S., An, K.I. and Hwang, J.S., 2014, Design of Condition Based Maintenance System for the Integrated and Intelligent Operation of Offshore Plant, Proceedings of the Society of CAD/CAM Engineers Conference, Republic of Korea, pp.152-155.
  17. Cho, S., Jun, H.B., Shin, J.H. and Choi, S., 2014, A Study on Estimating the Next Failure Time of LNG FPSO Compressor, Transactions of the Society of CAD/CAM Engineers, 19(3), pp.203-213. https://doi.org/10.7315/CADCAM.2014.203
  18. Yun, H.Y., Yang, Y.S. and Yun, J.H., 1992, A Stochastic Analysis of Crack Propagation Life under Constant Amplitude Loading, The Korean Society of Mechanical Engineers, 16(9), pp.1691-1699.
  19. Park, J., Kim, K., Kim, K.S. and Ko, D.Y., 2013, Comparison of Fatigue Damage Models of Spread Mooring Line for Floating Type Offshore Plant, Journal of Ocean Engineering and Technology, 20(5), pp.63-69.
  20. Lee, Y.S., Kim, D.J. and Ryu, H.H., 1998, Fatigue Crack Propagation and Fatigue Life Prediction under the Mixed Mode Loading, Proceedings of the Korean Society of Mechanical Engineers, pp.305-309.
  21. Lee, S.G., 2012, Technical Trend for LNG_FPSO, Journal of the Korean Society of Marine Engineering, 36(1), pp.62-78.
  22. Lee, K.S. and Son, C.Y., 2007, A Study on GUI Development of Structural Analysis of LNG Pump Tower, Journal of the Computational Structural Engineering Institute of Korea, 20(5), pp.605-613.
  23. Cedric, C., 2006, Development of a New Approach for Product Design Improvement, Considering Middle-of-life (MOL) Data, Diploma Thesis, EPFL.
  24. Huh, M.H. and Lee, G.H., 2004, Reproducibility Assessment of k-means Clustering and Applications, The Korean Journal of Applied Statistics, 17(1), pp.135-144. https://doi.org/10.5351/KJAS.2004.17.1.135
  25. Fraley, C. and Raftery, A.E., 1998, How Many Clusters? Which Clustering Method? Answers Via Model-based Cluster Analysis, The Computer Journal, 41(8), pp.578-588. https://doi.org/10.1093/comjnl/41.8.578
  26. Kim, S.Y., Jung, H.W., Park, J.D., Baek, S.M., Kim, W.S., Chon, K.H. and Song, K.B., 2014, Weekly Maximum Electric Load Forecasting for 104 Weeks by Seasonal ARIMA Model, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, 28(1), pp.50-56. https://doi.org/10.5207/JIEIE.2014.28.1.050
  27. Lopez-Espin, J.J., Vidal, A.M. and Gimenez, D., 2012, Two-stage Least Squares and Indirect Least Squares Algorithms for Simultaneous Equations Models, Journal of Computational and Applied Mathematics, 236(15), pp.3676-3684. https://doi.org/10.1016/j.cam.2011.07.005