Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
권호 표지
DOI QR코드

DOI QR Code

Prognostics for Industry 4.0 and Its Application to Fitness-for-Service Assessment of Corroded Gas Pipelines

인더스트리 4.0을 위한 고장예지 기술과 가스배관의 사용적합성 평가

  • Kim, Seong-Jun (Department of Industrial Engineering and Operations Management, Gangneung-Wonju National University) ;
  • Choe, Byung Hak (Department of Advanced Materials and Metal Engineering, Gangneung-Wonju National University) ;
  • Kim, Woosik (Gas Research Institute, Korea Gas Corporation)
  • 김성준 (강릉원주대학교 산업경영공학과) ;
  • 최병학 (강릉원주대학교 신소재금속공학과) ;
  • 김우식 (한국가스공사 가스연구원)
  • Received : 2017.11.02
  • Accepted : 2017.11.07
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: This paper introduces the technology of prognostics for Industry 4.0 and presents its application procedure for fitness-for-service assessment of natural gas pipelines according to ISO 13374 framework. Methods: Combining data-driven approach with pipe failure models, we present a hybrid scheme for the gas pipeline prognostics. The probability of pipe failure is obtained by using the PCORRC burst pressure model and First Order Second Moment (FOSM) method. A fuzzy inference system is also employed to accommodate uncertainty due to corrosion growth and defect occurrence. Results: With a modified field dataset, the probability of failure on the pipeline is calculated. Then, its residual useful life (RUL) is predicted according to ISO 16708 standard. As a result, the fitness-for-service of the test pipeline is well-confirmed. Conclusion: The framework described in ISO 13374 is applicable to the RUL prediction and the fitness-for-service assessment for gas pipelines. Therefore, the technology of prognostics is helpful for safe and efficient management of gas pipelines in Industry 4.0.

Keywords

References

  1. An, D., Kim, N. H., and Choi, Joo-Ho. 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews." Reliability Engineering and System Safety 133:223-236. https://doi.org/10.1016/j.ress.2014.09.014
  2. Bae, S. J. 2016. "Data-based Prognostics and Health Management," IE Magazine 23(4):9-12.
  3. Bazan, F. and Beck, A. 2013. "Stochastic process corrosion growth models for pipeline reliability." Corrosion Science 74:50-58. https://doi.org/10.1016/j.corsci.2013.04.011
  4. Cosham, A., Hopkins, P., and McDonald, K. 2007. "Best Practice for The Assessment of Defects in Pipelines." Engineering Failure Analysis 14:1245-1265. https://doi.org/10.1016/j.engfailanal.2006.11.035
  5. Dundulis, G., Zutautaitee, I., Janulionis, R., Usspuras, E., Rimkeviccius, S., and Eid, M. 2016. "Integrated failure probability estimation based on structural integrity analysis and failure data: Natural gas pipeline case." Reliability Engineering and System Safety 156:195-202. https://doi.org/10.1016/j.ress.2016.08.003
  6. Hasan, S., Khan, F., and Kenny, S. 2012. "Probability assessment of burst limit state due to internal corrosion." International Journal of Pressure Vessels and Piping 89:48-58. https://doi.org/10.1016/j.ijpvp.2011.09.005
  7. IBM. 2017. http://www.ibm.com/blogs/internet-of-things/industry-4-0-meets-cognitive-iot/, Accessed October 31.
  8. ISO. 2017. http://www.iso.org, Accessed October 31.
  9. Kim, S. B., and Kang, S. H., 2016. "Data science leads 4th Industrial Revolution." IE Magazine 23(3):9-13.
  10. Kim, S. J., Choe, B. H., Kim, W., and I. Kim. 2016. "A fuzzy inference based reliability method for underground gas pipelines in the presence of corrosion defects." Journal of Korean Institute of Intelligent Systems 26(5):343-350. https://doi.org/10.5391/JKIIS.2016.26.5.343
  11. Kwon, D., Hodkiewicz, M. R., Fan, J., Shibutani, T., and Michael Pecht. 2016. "IoT-Based Prognostics and Systems Health Management for Industrial Applications." IEEE Access 4:3660-3670.
  12. Javed, K., Gouriveau, R., and Zerhouni, N. 2017. "State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels." Mechanical Systems and Signal Processing 94:214-236. https://doi.org/10.1016/j.ymssp.2017.01.050
  13. Lee, J., Bagheri, B., and Kao, H. 2015. "A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems." Manufacturing Letters 3:18-23. https://doi.org/10.1016/j.mfglet.2014.12.001
  14. Lee, S., and Youn, B D.. 2015. "Industry 4.0 and Prognostics and Health Management." Noise and Vibration 25(1):22-28.
  15. Muller, A., Marquez, A., and Iung, B. 2008. "On the concept of e-maintenance." Reliability Engineering and System Safety 93:1165-1187. https://doi.org/10.1016/j.ress.2007.08.006
  16. Nunez, D. L., and Milton Borsato. 2017. "An ontology-based model for prognostics and health management of machines." Journal of Industrial Information Integration 6:33-46. https://doi.org/10.1016/j.jii.2017.02.006
  17. Ossai, C., Boswell, B., and Davies, I. 2015. "Pipeline failures in corrosive environments - A conceptual analysis of trends and effects." Engineering Failure Analysis 53:36-58. https://doi.org/10.1016/j.engfailanal.2015.03.004
  18. Pecht, M., and Rubyca Jaai. 2010. "A prognostics and health management roadmap for information and electronics- rich systems." Microelectronics Reliability 50:317-323. https://doi.org/10.1016/j.microrel.2010.01.006
  19. Seo, B., Jang, B., and Youn, B. D. 2015. "Prognostics and Its Engineering Applications with Vision." Noise and Vibration 25(1):7-15.
  20. Sikorska, J., Hodkiewicz, M., and Ma, L. 2011. "Prognostic modelling options for remaining useful life estimation by industry." Mechanical Systems and Signal Processing 25:1803-1836. https://doi.org/10.1016/j.ymssp.2010.11.018
  21. Zheng, X., and Fang, H. 2015. "An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction." Reliability Engineering and System Safety 144:74-82. https://doi.org/10.1016/j.ress.2015.07.013
  22. Zio, E. 2016. "Some Challenges and Opportunities in Reliability Engineering." IEEE Transactions on Reliability 65(4):1769-1782. https://doi.org/10.1109/TR.2016.2591504