Integrated e-Operations-e-Maintenance: Applications in North Sea Offshore Assets

Springer Series in Reliability Engineering

Volumn 8, Issue , 2008, Pages 585-609

  Liyanage, Jayantha P ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ENGINEERING; INDUSTRIAL ENGINEERING;

E MAINTENANCE; E-OPERATIONS; NORTH SEA;

OFFSHORE OIL WELL PRODUCTION;

EID: 84951787442     PISSN: 16147839     EISSN: 2196999X     Source Type: Book Series    
DOI: 10.1007/978-1-84800-011-7_24     Document Type: Chapter

References (51)

1. Arnaiz, A., Arana, R., Maurtua, I., et al., (2005), Maintenance: future technologies, Proceedings of the IMS (Intelligent Manufacturing System) International Forum IMS Forum 2004 Como, Italy, May 17-19, pp. 300-307.
2. Bangemann, T., Rebeuf, X., Reboul, D., et al., (2006), PROTEUS-creating distributed maintenance systems through an integration platform, Computers in Industry, 57(6), pp. 539-551.
3. Banjevic, D., Jardine, A. K. S., Makis, V. and Ennis, M., (2001), A control-limit policy and software for condition-based maintenance optimization, INFOR, 39, pp. 32-50.
4. Bonissone, G., (1995), Soft computing applications in equipment maintenance and service, ISIE '95, Proceedings of the IEEE International Symposium, 2, pp. 10-14.
5. Booher, HR. (ed.) (2003). Handbook of human systems integration, Wiley-Interscience.
6. Chande, A., Tokekar, R., (1998), Expert-based maintenance: a study of its effectiveness, IEEE Transactions on Reliability 47, pp. 53-58.
7. Chang, Y. S., Makatsoris, H. C., Richards, H. D., (2004), Evolution of supply chain management: symbiosis of adaptive value networks and ICT, Boston: Kluwer Academic Publishers.
8. Djurdjanovic, D., Ni, J., Lee, J., (2002), Time-frequency based sensor fusion in the assessment and monitoring of machine performance degradation, Proceedings of the 2002. ASME International Mechanical Engineering Congress and Exposition paper number IMECE 2002-32032.
9. Djurdjanovic, D., Lee, J., Ni, J., (2003), Watchdog agent-an infotronics-based prognostics approach for product performance degradation assessment and prediction, special issue on intelligent maintenance systems, Engineering Informatics Journal 17 (3-4), pp. 107-189.
10. During, W., Oakey, R., et al. (ed.) (2004). New technology-based firms in the new millennium. Elsevier.
11. Ellingssen, H. P., Liyanage, J. P., Ruså, R., (2006), Smart integrated operations and maintenance solutions to manage offshore assets in North Sea, Proceedings of the 18th EuroMaintenace, MM Support GmbH, pp, 319-324.
12. Emmanouilidis, C., MacIntyre, J., Cox, C., (1998), An integrated, soft computing approach for machine condition diagnosis, Proceedings of the Sixth European Congress on Intelligent Techniques & Soft Computing (EUFIT'98), vol. 2 Aachen, Germany, pp. 1221-1225.
13. Emmanouilidis, C., Jantunen E., MacIntyre, J., (2006), Flexible software for condition monitoring, Computers in Industry, 57(6), pp, 516-527.
14. García, M. C., Sanz-Bobi, M. A., (2002), Dynamic Scheduling of Industrial Maintenance Using Genetic Algorithms, Proceedings of EuroMaintenance 2002, Helsinki, Finland.
15. Garcia, M. C., Sanz-Bobi, M. A., Pico, J., (2006), SIMAP: Intelligent systems for predictive maintenance: Application to the health condition monitoring of a wind-turbine gearbox, Computers in Industry, 7(6), pp, 552-568.
16. Han, T., Yang, B. S., (2006), Development of an e-maintenance system integrating advanced techniques, Computers in Industry, 57(6), pp, 569-580.
17. Hansen, R., Hall, D., Kurtz, S., (1994), New approach to the challenge of machinery prognostics, Proceedings of the International Gas Turbine and Aeroengine Congress and Exposition American Society of Mechanical Engineers, pp. 1-8.
18. Health and Safety Executive (HSE). (1997). Human and organizational factors in offshore safety. HSE, UK.
19. Hosni, Y. A., Khalil, T. M. (ed.) (2004). Management of technology. Elsevier.
20. Iung, B., (2003), From remote maintenance to MAS-based e-maintenance of an industrial process, International Journal of Intelligent Manufacturing 14(1), pp. 59-82.
21. Jardine, A. K. S., Banjevic, D., Makis, V., (1997), Optimal replacement policy and the structure of software for condition-based maintenance, Journal of Quality in Maintenance Engineering, 3, pp. 109-119.
22. Jardine, A. K. S., Makis, V., Banjevic, D., et al., (1998), Decision optimization model for condition-based maintenance, Journal of Quality in Maintenance Engineering 4 (2), pp. 115-121.
23. Jardine, A. K. S. Lin, D., Banjevic, D., (2006) A review on machinery diagnostics and prognostics implementing condition based maintenance, Mech. Syst. Signal Process. 20 (7), pp. 1483-1510.
24. Jantunen, E. Jokinen, H. Milne, R., (1996), Flexible expert system for automated on-line diagnostics of tool condition, Integrated Monitoring & Diagnostics & Failure Prevention, Technology Showcase, 50th MFPT Mobile, Alabama.
25. Khatib, A. R., Dong, Z., Qiu, B., et al., (2000), Thoughts on future Internet based power system information network architecture, in: Proceedings of the 2000 Power Engineering Society Summer Meeting, vol. 1, Seattle, USA.
26. Koc, M., Lee, J., (2001), A system framework for next-generation e-maintenance system, Proceeding of Second International Symposium on Environmentally Conscious Design and Inverse Manufacturing Tokyo, Japan.
27. Lee, J. (1996), Measurement of machine performance degradation using a neural network model, Computers in Industry 30, pp. 193-209.
28. Lee, J., (2004), Infotronics based intelligent maintenance system and its impacts to closed loop product life cycle systems, Proceedings of the Proceedings of the IMS'2004 International Conference on Intelligent Maintenance Systems Arles, France.
29. Liao, H. T., Lin, D. M. Qiu, H., et al., (2005), A predictive tool for remaining useful life estimation of rotating machinery components, ASME International 20th Biennial Conference on Mechanical Vibration and Noise Long Beach, CA.
30. Liyanage, J. P., (2003), Operations and maintenance performance in oil and gas production assets: Theoretical architecture and capital value theory in perspective, PhD Thesis, Norwegian University of Science and Technology (NTNU), Norway.
31. Liyanage, J. P., Herbert, M., Harestad, J., (2006), Smart integrated e-operations for high-risk and technologically complex assets: Operational networks and collaborative partnerships in the digital environment, Wang, Y. C., et al., (ed.), Supply chain management: Issues in the new era of collaboration and competition, Idea Group, USA, pp. 387-414.
32. Liyanage, J. P., Langeland, T., (2007), Smart assets through digital capabilities, Mehdi Khosrow-Pour (ed.), Encyclopaedia of Information Science and Technology, Idea Group, USA.
33. Liang, E., Rodriguez, R., Husseiny, A., (1988), Prognostics/diagnostics of mechanical equipment by neural network, Neural Networks 1 (1), p. 33.
34. Marseguerra, M., Zio, E., Podofilini, L., (2002), Condition-based optimisation by means of genetic algorithms and Monte Carlo simulation, Reliability Engineering and System Safety 77, pp. 151-166.
35. Mezgaar, I., (2006), Integration of ICT in smart organizations, Hershey, PA: Idea Group Pub.
36. Moore, W. J., Starr, A. G., (2006), An intelligent maintenance system for continuous costbased prioritization of maintenance activities, Computers in Industry, 57(6), pp. 595-606.
37. OLF (Oljeindustriens landsforening/Norwegian Oil Industry Association), (2003). eDrift for norsk sokkel: det tredje effektiviseringsspranget (eOperations in the Norwegian continental shelf: The third efficiency leap), OLF (www. olf. no). (in Norwegian).
38. Palluat, N., Racoceanu, D., Zerhouni, N., (2006), A neuro-fuzzy monitoring system: Application to flexible production systems, Computers in Industry, 57(6), pp. 528-538.
39. Perow, C. (1999). Normal accidents: Living with high-risk technologies, Pinceton University Press.
40. Roemer, M. Kacprzynski, G., Orsagh, R. (2001), Assessment of data and knowledge fusion strategies for prognostics and health management, IEEE Aerospace Conference Proceedings, vol. 6, pp. 62979-62988.
41. Russell, R. S., Taylor, B. W., (2006), Operations management: Quality and competitiveness in a global environment, Hoboken, N. J.: Wiley.
42. Sanz-Bobi, M. A., Toribio, M. A. D., (1999), Diagnosis of electrical motors using artificial neural networks, IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Gijón, Spain, pp. 369-374.
43. Sanz-Bobi, M. A., Palacios, R. Munoz, A., et al., (2002), ISPMAT: Intelligent System for Predictive Maintenance Applied to Trains, Proceedings of EuroMaitenance 2002, Helsinki, Finland.
44. Swanson, L., (2001), Linking maintenance strategies to performances, International Journal of Production Economics 70, pp. 237-244.
45. van Oostendrep, H., Breure, L., Dillon, A., (2005), Creation, use, and deployment of digital information, Mahwah, N. J.: Lawrence Erlbaum Associates.
46. Wang, W., (2002), A stochastic control model for on line condition based maintenance decision support, Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics, Part 6, vol. 6, pp. 370-374.
47. Wang, W. Y. C., Heng, M. S. H., Chau, P. Y. K., (2006), Supply chain management: Issues in the new era of collaboration and competition, Idea Group Publishing.
48. Yager R., Zadeh, L., (1992), An Introduction to Fuzzy Logic Applications in Intelligent Systems, Kluwer Academic Publishers.
49. Yang, B. S., Lim, D. S., Lee, C. M., (2000), Development of a case-based reasoning system for abnormal vibration diagnosis of rotating machinery, Proceedings of the International Symposium on Machine Condition Monitoring and Diagnosis Japan, pp. 42-48.
50. Yen, G. G., (2003), Online multiple-model-based fault diagnosis and accomodation, IEEE Transaction on Industrial Electronics 50 (2).
51. Yu, R., Iung B., Panetto, H., (2003), A mutli-agents based e-maintenance system with casebased reasoning decision support, Engineering Applications of Artificial Intelligence 16, pp. 321-333.