An Intelligent Fleet Condition-Based Maintenance Decision Making Method Based on Multi-Agent
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Published
Mar 22, 2021
Qiang Feng
Songjie Li
Bo Sun
Abstract
According to the demand for condition-based maintenance online decision making among a mission oriented fleet, an intelligent maintenance decision making method based on Multi-agent and heuristic rules is proposed. The process of condition-based maintenance within an aircraft fleet (each containing one or more Line Replaceable Modules) based on multiple maintenance thresholds is analyzed. Then the process is abstracted into a Multi-Agent Model, a 2-layer model structure containing host negotiation and independent negotiation is established, and the heuristic rules applied to global and local maintenance decision making is proposed. Based on Contract Net Protocol and the heuristic rules, the maintenance decision making algorithm is put forward. Finally, a fleet consisting of 10 aircrafts on a 3-wave continuous mission is illustrated to verify this method. Simulation results indicate that this method can improve the availability of the fleet, meet mission demands, rationalize the utilization of support resources and provide support for online maintenance decision making among a mission oriented fleet.
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Keywords
condition based maintenance (CBM), fleet-level optimization, decision making, multi-agent system, heuristic rule
References
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Bengtsson, M. (2004). Condition Based Maintenance System Technology - Where is Development Heading. Congress Report on the 17th Euromaintenance, May, Barcelona, Spain. http://www.idp.mdh.se/forskning/amnen/produktprocess/projekt/cbm/publi-kationer/em04%20paper%20Marcus%20Bengtsson.pdf.
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Goebel, K.F., Saha B., Saxena, A., Celaya, J.R. & Christophersen, J.P. (2008). Prognostics in Battery Health Management. Instrumentation & Measurement Magazine, IEEE, vol. 11(4), pp. 33-40. doi:10.1109/MIM.2008.4579269
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Barata, J., Guedes, S.C., Marseguerra, M. & Zio, E. (2002). Simulation modeling of repairable multi-component deteriorating systems for ‘on condition’ maintenance optimization. Reliability Engineering & System Safety, vol. 76(3), pp. 255–264. doi:10.1016/S0951-8320(02)00017-0
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Garey, M.R. & Johnson, D.S. (1979). Computers and Intractability: A Guide to the Theory of NP- Completeness. New York, USA: WH Freeman & Co.
Doganay, K. & Bohlin, M. (2010). Maintenance plan optimization for a train fleet. WIT Transactions on The Built Environment, vol. 114(12), pp. 349-358. doi:10.2495/CR100331
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Reimann, J., Kacprzynski, G., Cabral, D. & Marini, R. (2009). Using Condition Based Maintenance to Improve the Profitability of Performance Based Logistic Contracts. Annual Conference of the Prognostics and Health Management Society, Sep 27-Oct 1, San Diego, California. http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2009/phmc_09_52.pdf
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Dupuy, M.J., Wesely, D.E. & Jenkins, C.S. (2011). Airline fleet maintenance: Trade-off analysis of alternate aircraft maintenance approaches. Systems and Information Engineering Design Symposium (SIEDS). April 29, Charlottesville, VA. doi:10.1109/SIEDS.2011.5876850
Cycon, J.P. (2011). The journey to incorporate health monitoring and condition based maintenance of Sikorsky commercial helicopters. 8th International Workshop on Structural Health Monitoring 2011: Condition-Based Maintenance and Intelligent Structures, September 13-15, Stanford, CA. http://www.destechpub.com/links/catalogs/bookstore/advanced-materials-sciencetechnology/structural-health-monitoring/structural-health-monitoring-2011/#table
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Budenske J., Newhouse J., Bonney J. & Wu J. (2001). Agent-based schedule validation and verification. Systems, Man, and Cybernetics, 2001 IEEE International Conference on. Oct 7-10, Tucson, AZ. doi:10.1109/ICSMC.2001.969920
Yang, S.L. & Hu, X.J. (2007). Complex decision task modeling and solving methods. Beijing: Science Press
Camci, F., Valentine, G.S. & Navarra, K. (2007). Methodologies for Integration of PHM Systems with Maintenance Data. Aerospace Conference, 2007 IEEE, March 3-10, Big Sky, MT. doi:10.1109/AERO.2007.352917
Zhang, G.J. & Li, Y.D. (2010). Agent-based modeling and simulation for open complex systems. Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, March 6-7, Wuhan. doi: 10.1109/CAR.2010.5456783
Baker, A.D. (1998). A survey of factory control algorithms that can be implemented in a multi-agent heterarchy: dispatching, scheduling and pull. Journal of Manufacturing Systems, vol. 17(4), pp. 297–320. doi: 10.1016/S0278-6125(98)80077-0
Durfee, E.H. (1988). Coordination of Distributed Problem Solvers. Boston, MA: Kluwer Academic Publishers.
Feng, Q, Zeng, S.K. & Kang, R. (2010) Multiagent-based modeling method for integrated logistic support of the carrier aircraft. Systems Engineering and Electronics, vol. 32(1), pp. 211-217.
Smith, R.G. (1980). The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. Computers, IEEE Transactions on, vol. C-29(12), pp. 1104-1113. doi: 10.1109/TC.1980.1675516
Tang, S.Y., Zhu, Y.F., Li, Q. & Lei, Y.L. (2010). Survey of task allocation in multi Agent systems. Systems Engineering and Electronics, vol. 32(10), pp. 2155-2161. doi:0.3969/j/issn/1001-506X.2010.10.30
Feng, Q. (2009). Mesuring and Modeling and Optimization Method of Effectiveness for Complex Materiel System based on Multi-Agent. Doctoral dissertation. Beihang University, Beijing, China
Bauer, B., Müller, J.P. & Odell, J. (2001). Agent UML: a formalism for specifying multiagent software systems. International Journal of Software Engineering and Knowledge Engineering, vol. 11(3), pp. 1-12. doi:10.1142/S0218194001000517
Bengtsson, M. (2004). Condition Based Maintenance System Technology - Where is Development Heading. Congress Report on the 17th Euromaintenance, May, Barcelona, Spain. http://www.idp.mdh.se/forskning/amnen/produktprocess/projekt/cbm/publi-kationer/em04%20paper%20Marcus%20Bengtsson.pdf.
Sun, B., Zeng, S.K., Kang, R. & Pecht, M. G. (2012). Benefits and Challenges of System Prognostics. Reliability. IEEE Transactions on, vol. 61(2), pp. 323-335. doi:10.1109/TR.2012.2194173
Goebel, K.F., Saha B., Saxena, A., Celaya, J.R. & Christophersen, J.P. (2008). Prognostics in Battery Health Management. Instrumentation & Measurement Magazine, IEEE, vol. 11(4), pp. 33-40. doi:10.1109/MIM.2008.4579269
Wen, Z.H. & Liu, Y.P. (2011). Applications of Prognostics and Health Management in aviation industry. Prognostics and System Health Management Conference. May 24-25, Shenzhen, China. doi:10.1109/PHM.2011.5939539
Barata, J., Guedes, S.C., Marseguerra, M. & Zio, E. (2002). Simulation modeling of repairable multi-component deteriorating systems for ‘on condition’ maintenance optimization. Reliability Engineering & System Safety, vol. 76(3), pp. 255–264. doi:10.1016/S0951-8320(02)00017-0
Jardine, A.K.S., Lin, D. & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, vol. 20(7), pp. 1483-1510. doi:10.1016/j.ymssp.2005.09.012
Garey, M.R. & Johnson, D.S. (1979). Computers and Intractability: A Guide to the Theory of NP- Completeness. New York, USA: WH Freeman & Co.
Doganay, K. & Bohlin, M. (2010). Maintenance plan optimization for a train fleet. WIT Transactions on The Built Environment, vol. 114(12), pp. 349-358. doi:10.2495/CR100331
Bai, F. (2009). Methods of Scheduling and Condition Based Mainteance Decision Making in Civil Aero Engine Fleet. Doctoral dissertation. Nanjing University of Aeronautics and Astronautics, Nanjing, China
Reimann, J., Kacprzynski, G., Cabral, D. & Marini, R. (2009). Using Condition Based Maintenance to Improve the Profitability of Performance Based Logistic Contracts. Annual Conference of the Prognostics and Health Management Society, Sep 27-Oct 1, San Diego, California. http://www.phmsociety.org/sites/phmsociety.org/files/phm_submission/2009/phmc_09_52.pdf
Bivona, E. & Montemaggiore, G.B. (2005). Evaluating Fleet and Maintenance Management Strategies through System Dynamics Model in a City Bus Company. The 23rd International Conference of the System Dynamics Society, July 17-21, Boston. http://www.systemdynamics.org/conferences/2005/proceed/papers/MONTE431.pdf.
Dupuy, M.J., Wesely, D.E. & Jenkins, C.S. (2011). Airline fleet maintenance: Trade-off analysis of alternate aircraft maintenance approaches. Systems and Information Engineering Design Symposium (SIEDS). April 29, Charlottesville, VA. doi:10.1109/SIEDS.2011.5876850
Cycon, J.P. (2011). The journey to incorporate health monitoring and condition based maintenance of Sikorsky commercial helicopters. 8th International Workshop on Structural Health Monitoring 2011: Condition-Based Maintenance and Intelligent Structures, September 13-15, Stanford, CA. http://www.destechpub.com/links/catalogs/bookstore/advanced-materials-sciencetechnology/structural-health-monitoring/structural-health-monitoring-2011/#table
Zhou, R., Fox, B., Lee, H.P. & Nee., A.Y.C. (2004). Bus maintenance scheduling using multi-agent systems. Engineering Applications of Artificial Intelligence, vol. 17(6), pp. 623–630. doi:10.1016/j.engappai.2004.08.007
Papakostas, N., Papachatzakis, P., Xanthakis, V., Mourtzis, D. & Chryssolouris, G. (2010). An approach to operational aircraft maintenance planning. Decision Support Systems, vol. 48(4), pp. 604–612. doi:10.1016/j.dss.2009.11.
Budenske J., Newhouse J., Bonney J. & Wu J. (2001). Agent-based schedule validation and verification. Systems, Man, and Cybernetics, 2001 IEEE International Conference on. Oct 7-10, Tucson, AZ. doi:10.1109/ICSMC.2001.969920
Yang, S.L. & Hu, X.J. (2007). Complex decision task modeling and solving methods. Beijing: Science Press
Camci, F., Valentine, G.S. & Navarra, K. (2007). Methodologies for Integration of PHM Systems with Maintenance Data. Aerospace Conference, 2007 IEEE, March 3-10, Big Sky, MT. doi:10.1109/AERO.2007.352917
Zhang, G.J. & Li, Y.D. (2010). Agent-based modeling and simulation for open complex systems. Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, March 6-7, Wuhan. doi: 10.1109/CAR.2010.5456783
Baker, A.D. (1998). A survey of factory control algorithms that can be implemented in a multi-agent heterarchy: dispatching, scheduling and pull. Journal of Manufacturing Systems, vol. 17(4), pp. 297–320. doi: 10.1016/S0278-6125(98)80077-0
Durfee, E.H. (1988). Coordination of Distributed Problem Solvers. Boston, MA: Kluwer Academic Publishers.
Feng, Q, Zeng, S.K. & Kang, R. (2010) Multiagent-based modeling method for integrated logistic support of the carrier aircraft. Systems Engineering and Electronics, vol. 32(1), pp. 211-217.
Smith, R.G. (1980). The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. Computers, IEEE Transactions on, vol. C-29(12), pp. 1104-1113. doi: 10.1109/TC.1980.1675516
Tang, S.Y., Zhu, Y.F., Li, Q. & Lei, Y.L. (2010). Survey of task allocation in multi Agent systems. Systems Engineering and Electronics, vol. 32(10), pp. 2155-2161. doi:0.3969/j/issn/1001-506X.2010.10.30
Feng, Q. (2009). Mesuring and Modeling and Optimization Method of Effectiveness for Complex Materiel System based on Multi-Agent. Doctoral dissertation. Beihang University, Beijing, China
Bauer, B., Müller, J.P. & Odell, J. (2001). Agent UML: a formalism for specifying multiagent software systems. International Journal of Software Engineering and Knowledge Engineering, vol. 11(3), pp. 1-12. doi:10.1142/S0218194001000517
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