Abstract
Engineering asset management (EAM) is a broad discipline with distributed functions and services. When engineering assets are capital intensive, management requires specialized expertise for diagnosis, prognosis, maintenance and repairs. The current practice of EAM relies on self maintained experiential rules with coordinated collaboration and outsourcing for maintenance and repairs. In order to enhance the life long asset value and efficiency (from the stakeholder’s viewpoint) and after sales service quality (from the asset provider’s viewpoint), this research proposes a collaborative maintenance platform that integrates real time data collection with diagnostic and prognostic expertise. The collaborative system combines and delivers services among asset operation sites (the maintenance demanders), the service center (the intermediary coordinator), the system providers, the first tier maintenance collaborators, and the second and lower tier parts suppliers. Multi-agent system technology is used to integrate different systems and databases. Agents with autonomy and authority work to assist service providers and coordinate communications, negotiations, and maintenance decision support. Finally, game theory is used to design the decision models for strategic, tactical, and operational decision making during collaborative maintenance practices.
Similar content being viewed by others
References
Bangemann T., Rebeuf X., Reboul D., Schulze A., Szymanski J., Thomesse J. P., Thron M., Zerhouni N. (2006) PROTEUS—creating distributed maintenance systems through an integration platform. Computers in Industry 57(6): 539–551
Bertling L., Allan R., Eriksson R. (2005) A reliability-centered asset maintenance method for assessing the impact of maintenance in power distribution systems. IEEE Transactions on Power Systems 20(1): 75–82
Bretthauer G., Gamaleja T., Handschin E., Neumann U., Hoffmann W. (1998) Integrated maintenance scheduling system for electrical energy system. IEEE Transactions on Power Delivery 13(2): 655–660
Chattopadhyay D. (2004) A game theoretic model for strategic maintenance and dispatch decisions. IEEE Transactions on Power Systems 19(4): 2014–2021
CIEAM (CRC for Integrated Engineering Asset Management). (2008). Available at http://www.cieam.com/, Accessed on March 15, 2008.
Fu C., Ye L., Liu Y., Yu R., Iung B., Cheng Y., Zeng Y. (2004) Predictive maintenance in intelligent control maintenance management system for hydroelectric generating unit. IEEE Transactions on Energy Conversion 19(1): 1–8
Han T., Yang B. S. (2006) Development of an e-maintenance system integrating advanced techniques. Computers in Industry 57(6): 569–580
Hipel K. W., Jamshidi M. M., Tien J. M., White C. C. III (2007) The future of systems, man, and cybernetics: Application domains and research methods. IEEE Transactions on Systems, Man, and Cybernetics 37(5): 726–743
Hossack J. A., Menal J., McArthur S. D. J., McDonald J. R. (2003) A multiagent architecture for protection engineering diagnostic assistance. IEEE Transactions on Power Systems 18(2): 639–647
Hsiao D. W., Trappey A. J. C., Ma L., Fan Y.-C., Mao Y.-C. (2008) Agent-based integrated and collaborative engineering asset management. Materials Science Forum 594: 481–493
Iung B. (2003) From remote maintenance to MAS-based e-maintenance of an industrial process. Journal of Intelligent Manufacturing 14(1): 59–82
JADE (Java Agent DEvelopment framework). (2007). http://jade.tilab.com, Accessed May 28, 2007
Jenab K., Zolfaghari S. (2008) A virtual collaborative maintenance architecture for manufacturing enterprises. Journal of Intelligent Manufacturing 19(6): 763–771
Kreps D. M. (1990) Game theory and economic modeling. Oxford University Press Inc, New York
Li, Y., Chun, L., & Ching, A. N. Y. (2005). An agent-based platform for web-enabled equipment predictive maintenance. In Proceedings of the 2005 IEEE/WIC/ACM international conference on intelligent agent technology (IAT’05) (pp. 132–135).
Majidian A., Saidi M. H. (2007) Comparison of fuzzy logic and neural network in life prediction of boiler tubes. International Journal of Fatigue 29: 489–498
McArthur S. D. J., Booth C. D., McDonald J. R., McFadyen I. T. (2005) An agent-based anomaly detection architecture for condition monitoring. IEEE Transactions on Power Systems 20(4): 1675–1682
Nagarajan M., Sosic G. (2008) Game-theoretic analysis of cooperation among supply chain agents: Review and extensions. European Journal of Operational Research 187: 719–745
Sun Y., Ma L., Mathew J., Zhang S. (2006) An analytical model for interactive failures. Reliability Engineering & System Safety 91(5): 495–504
Tien J.M. (2005) Viewing urban disruptions from a decision informatics perspective. Journal of Systems Science and Systems Engineering 14(3): 257–288
Yang Z., Djurdjanovic D., Ni J. (2008) Maintenance scheduling in manufacturing systems based on predicted machine degradation. Journal of Intelligent Manufacturing 19(1): 87–98
Yao Y. H., Lin G. Y. P., Trappey A. J. C. (2005) Using knowledge-based intelligent reasoning to support dynamic equipment diagnosis and maintenance. International Journal of Enterprise Information Systems 2(1): 17–29
FIPA (Foundation for Intelligent Physical Agents). (2008). Agent communication language specification, http://www.fipa.org, Accessed June 1, 2008.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Trappey, A.J.C., Trappey, C.V. & Ni, WC. A multi-agent collaborative maintenance platform applying game theory negotiation strategies. J Intell Manuf 24, 613–623 (2013). https://doi.org/10.1007/s10845-011-0606-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-011-0606-5