산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제37권4호
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  • Pages.12-23
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  • 2014
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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LNG FPSO 압축기 고장시간 예측 방안에 관한 연구

A Study on Estimating the Next Failure Time of a Compressor in LNG FPSO

  • 조상제 (홍익대학교 산업공학과) ;
  • 전홍배 (홍익대학교 산업공학과) ;
  • 신종호 (울산과기대 디자인 및 인간공학과) ;
  • 황호진 (선박해양플랜트연구소 해양플랜트산업기술센터)
  • Cho, Sang-Je (Dept. of Industrial Engineering, Hongik University) ;
  • Jun, Hong-Bae (Dept. of Industrial Engineering, Hongik University) ;
  • Shin, Jong-Ho (Dept. of Design and Human Engineering, UNIST) ;
  • Hwang, Ho-Jin (Korea Research Institute of Ships and Ocean Engineering)
  • 투고 : 2014.06.11
  • 심사 : 2014.11.11
  • 발행 : 2014.12.31
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초록

The O&M (Operation and Maintenance) phase of offshore plants with a long life cycle requires heavy charges and more efforts than the construction phase, and the occurrence of an accident of an offshore plant causes catastrophic damage. So previous studies have focused on the development of advanced maintenance system to avoid unexpected failures. Nowadays due to the emerging ICTs (Information Communication Technologies) and sensor technologies, it is possible to gather the status data of equipment and send health monitoring data to administrator of an offshore plant in a real time way, which leads to having much concern on the condition based maintenance policy. In this study, we have reviewed previous studies associated with CBM (Condition-Based Maintenance) of offshore plants, and introduced an algorithm predicting the next failure time of the compressor which is one of essential mechanical devices in LNG FPSO (Liquefied Natural Gas Floating Production Storage and Offloading vessel). To develop the algorithm, continuous time Markov model is applied based on gathered vibration data.

키워드

참고문헌

  1. Antoine, G., Laurence, D., Christophe, B., and Michel, R., Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Transactions on Reliability, 2002, Vol. 51, No. 2, p 141-150. https://doi.org/10.1109/TR.2002.1011518
  2. Arunraj, N.S. and Maiti, J., Risk-based maintenance policy selection using AHP and goal programming. Safety Science, 2010, Vol. 48, No. 2, p 238-247. https://doi.org/10.1016/j.ssci.2009.09.005
  3. Bae, J.H. et al., A study on risk quantifying method for the worker accidents of offshore plant considering uncertainty. Proceedings of the society of naval architects of Korea, 2012, p 113-118.
  4. Carey, B. and Dan, M, Condition-based maintenance of machines using hidden markov models. Mechanical Systems and Signal Processing, 2000, Vol. 14, No. 4, p 597-612. https://doi.org/10.1006/mssp.2000.1309
  5. Choe, B.H., Lee, S.H., Kim, Y.P., Kim, W.S., and Ko, Y.T., Probabilistic Assessment of life Time for Gas Pipe Lines. Proceedings of Trans. Korean Soc. Mech. Eng., 2004, p 234-239.
  6. Dey, P.K., Ogunlana, S.O., and Naksuksakul, S., Riskbased maintenance model for offshore oil and gas pipelines : a case study. Journal of Quality in Maintenance Engineering, 2004, Vol. 10, No. 3, p 169-183. https://doi.org/10.1108/13552510410553226
  7. Durdjanovic, D., Lee, J., and Ni, J., Watchdog agent-an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics, 2003, Vol. 17, No. 3-4, p 109-125. https://doi.org/10.1016/j.aei.2004.07.005
  8. Fu, C., Ye, L., Liu, Y., Yu, R., Iung, B., Cheng, Y., and Zeng, Y., Predictive maintenance in intelligent-control- maintenance-management system for hydroelectric generating unit. IEEE Transactions on Energy Convertsion, 2004, Vol. 19, No. 1, p 179-186. https://doi.org/10.1109/TEC.2003.816600
  9. Goncharenko, I. and Kimura, F., Remote maintenance for IM. Proceedings of the first international symposium on environmentally conscious design and inverse manufacturing (EcoDesign'99), 1999, p 862-867.
  10. Grall, A., Dieulle, L., Berenguer, C., and Roussignol, M., Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Transactions on Reliability, 2002, Vol. 51, No. 2, p141-150. https://doi.org/10.1109/TR.2002.1011518
  11. Hussin, H., Hashim, F.M., Muhammad, M., and Ibrahim, S.N., A systematic and practical approach of analyzing offshore system maintenance data. Proceedings of the International Multiconference of Engineers and Computer Scientists, 2010, Vol. 3.
  12. ISO 7919, ISO Standard : Mechanical Vibration of Non- Reciprocating Machine-Measurements on Rotating Shafts and Evaluation, Technical report, 1996.
  13. Jang, D.H. et al., Developing the maintenance system for offshore plant and ship. Proceedings of the society of naval architects of Korea, 2011, p 1262-1271.
  14. Jeon, J., Lee, J.H., and Son G.J., Development of PEID for acquiring maintenance information during product lifecycle of marine vessels. Journal of the Korean society of ocean engineers, 2012, Vol. 26. No. 5, p 63-72.
  15. Jun, H.B., Conte, F.L., Kiritsis, D., and Xirouchakis, P., A predictive algorithm for estimating the quality of vehicle engine oil. International Journal of Industrial Engineering : Theory, Applications and Practice, 2009, Vol. 15, No. 4, p 386-396.
  16. Kim, Y.S., CBM approach for facility equipment, Technical report. Procon co., No. 2, 2012, p 96-103.
  17. Koc, M. and Lee, J., A system framework for next-generation E-maintenance systems. Transaction of Chinese Mechanical Engineer, 2001, Vol. 12, No. 5.
  18. Lee, B.J, Vibration and maintenance handbook for field engineers, Technical report, 1999, p 18-94.
  19. Lee, H.W., Kim, J.J., Park, S.H., MTBF estimator in reliability growth model with application to Weibull process. Journal of the Korean Society for Quality Management, 1998, Vol. 26, No.3, pp 71-81.
  20. Lee, K.H. and Lee, J.M., A study on augmented reality technologies in the operation and maintenance phase of offshore plants. Proceedings of the society of naval architects of Korea, 2011, p 106-111.
  21. Lee, J., E-intelligence strategies for product and manufacturing innovation. Transaction of Chinese Mechanical Engineer, 2001, Vol. 12, No. 5, p 526-531.
  22. Lee, S.K., State-of-the-art for LNG-FPSO technology. Journal of the Korean Society of Marine Engineering, 2012, Vol. 36, No. 1, p 62-78.
  23. Lin, D., Wiseman, M., Banjevic, D., and Jardine, A.K., An approach to signal processing and condition-based maintenance for gearboxes subject to tooth failure. Mechanical Systems and Signal Processing, 2004, Vol. 18, No. 5, p 993-1007. https://doi.org/10.1016/j.ymssp.2003.10.005
  24. Lu, H., Kolarik, W.J., and Lu, S.S., Real-Time Performance Reliability Prediction. IEEE Transaction on Reliability, 2001, Vol. 50, No. 4, p 353-357. https://doi.org/10.1109/24.983393
  25. Moan, T., Reliability-based management of inspection, maintenance and repair of offshore structures. Structure and Infrastructure Engineering, 2005, Vol. 1, No. 1, p 33-62. https://doi.org/10.1080/15732470412331289314
  26. Mobley, R.K., An Introduction to Predictive Maintenance. Elsevier science publisher, 2002.
  27. Park, S.W. and Lee, H.M., Design of Hull Residual Life Prediction System Considering Corrosion and Coating. Journal of the society of naval architects of Korea, 2013, Vol. 50, No. 2, p 104-110. https://doi.org/10.3744/SNAK.2013.50.2.104
  28. Rouhan, A. and Schoefs, F., Probabilistic modeling of inspection results for offshore structures. Structural safety, 2003, Vol. 25, No. 4, p 379-399. https://doi.org/10.1016/S0167-4730(03)00016-X
  29. Saranga, H. and Knezevic, J., Reliability prediction for condition-based maintained systems. Reliability Engineering and System Safety, 2001, Vol. 71, No. 2, p 219- 224. https://doi.org/10.1016/S0951-8320(00)00094-6
  30. Seo, K.K. and Seo, J.H., Decision-making Method of Optimum Inspection Interval for Plant Maintenance by Genetic Algorithms. Journal of the Society of Korea Industrial and Systems Engineering, 2003, Vol. 26, No. 2, p 1-8.
  31. Suprasad, V., Leland, M., and Hoang, P., Cost effective condition based maintenance using markov decision processes. Proceedings of IEEE Annual Maintainability Symposium on RAMS, 2006, p 464-469.
  32. Toshio, T., Survey on predictive maintenance technologies for production plant, Technical report. New-tech co., 2010, p 100-108.
  33. Wang, W. and Majid, H.B.A., Reliability data analysis and modelling of offshore oil platform plant. Journal of Quality in Maintenance Engineering, 2000, Vol. 6, No. 4, p 287-295. https://doi.org/10.1108/13552510010346824
  34. Yang, B.S., Condition monitoring and diagnostics, Intervision publisher, 2006.

피인용 문헌

  1. 센서 데이터를 이용한 전기 기관차의 이상 상태 요인분석 vol.41, pp.2, 2014, https://doi.org/10.11627/jkise.2018.41.2.084