A System of Systems Architecture for Optimizing Aircraft Health Management in Civil Aviation
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Published
Jan 13, 2026
Takuro Koizumi
Nozomu Kogiso
Abstract
This study proposes a system of systems (SoS) architecture for efficient aircraft health management (AHM) in civil aviation from the perspective of an aircraft manufacturer and formulates AHM as a multi-objective optimization problem. First, the SoS architecture is described to capture the interrelationship of the strategic capabilities required among the relevant stakeholders including airline customers, and the regulatory authority by using the Unified Architecture Framework (UAF). Parameters to measure strategic capabilities and operational activities are identified and the relationships between them are defined using parametric causal correlation. Next, AHM performance, effect, and amount of required data are formulated in terms of the identified variables in the SoS architecture description. Quantification enables the maximization of the effectiveness of AHM implementation by formulating it as a multi objective optimization problem, which allows for the quantitative assessment of the relationships between the context of AHM implementation and strategic capabilities. This formulation makes it possible to evaluate AHM effectiveness quantitatively, improving upon our previously proposed SoS architecture model, which only evaluated the relationship between stakeholders qualitatively.
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Keywords
System of Systems, Aircraft Health Management, Aircraft Maintenance, Unified Architecture Framework, System Dynamics, Optimization
References
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Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.
Hölzel, N. B., & Gollnick, V. (2015). Cost-benefit analysis of prognostics and condition-based maintenance concepts for commercial aircraft considering prognostic errors. Annual Conference of the Prognostics and Health Management Society.
Hu, Y., Miao, X., Si, Y., Pan, E., & Zio, E. (2022). Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering & System Safety, vol. 217, pp. 108063.
Hussain, Y. M., Burrow, S., Henson, L., & Keogh, P. (2016). Benefits analysis of prognostics & health monitoring to aircraft maintenance using system dynamics. European Conference of the Prognostics and Health Management Society.
IATA. (2022a). Aircraft Operational Availability (2nd ed.). https://www.iata.org/contentassets/bf8ca67c8bcd4358b3d004b0d6d0916f/aoa-2nded-2022.pdf
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Koizumi, T. (2023). Development method of maintenance program in commercial aircraft development. Journal of the Japan Society for Aeronautical and Space Sciences, vol. 71(4), pp. 91-98. (in Japanese)
Koizumi, T., & Kogiso, N. (2024a). An effectiveness evaluation method using system of systems architecture description of aircraft health management in aircraft maintenance program. International Journal of Prognostics and Health Management, vol. 15(3), pp. 3837.
Koizumi, T., & Kogiso, N. (2024b). Trade-off analysis from system of systems perspective for AHM adoption to civil aircraft maintenance. Proceedings of the 34th Design Engineering and Systems Conference of JSME, 1409. (in Japanese with English abstract)
Koizumi, T., Morino, H., Hara, T., & Aoyama, K. (2025). Stakeholder harmonization of hydrogen supply business: An UAF approach to system dynamics in enterprise architecture and product service systems. 35th Annual INCOSE International Symposium.
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Olsson, E., Funk, P., Candell, O., Sohlberg, R., & Karim, R. (2021). Enterprise modeling for dynamic matching of tactical needs and aircraft maintenance capabilities. 15th International Conference and Workshop on Industrial AI.
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Sriram, C., & Haghani, A. (2003). An optimization model for aircraft maintenance scheduling and re-assignment. Transportation Research Part A: Policy and Practice, vol. 37(1), pp. 29-48.
Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston, MA: Irwin/McGraw-Hill.
Teubert, C., Pohya, A. A., & Gorospe, G. (2023). Analysis of barriers preventing the widespread adoption of predictive and prescriptive maintenance in aviation. NASA TM–20230000841.
The Object Management Group [OMG]. (2022). Enterprise architecture guide for UAF (Informative). Formal/22-07-10. https://www.omg.org/spec/UAF/1.2
Ueda, Y., & Hidaka, K. (2024). A study of homogeneity and heterogeneity in maintenance costs for commercial aircraft. Transactions of the Japan Society for Aeronautical and Space Sciences, vol. 72(2), pp. 41-47. (in Japanese with English abstract)
U.S. Department of Transportation. (2024). Bureau of Transportation Statistics. https://www.transtats.bts.gov/homepage.asp
Verhoeff, M., Verhagen, W. J. C., & Curran, R. (2015). Maximizing operational readiness in military aviation by optimizing flight and maintenance planning. Transportation Research Procedia, vol. 10, pp. 941-950.
Vianna, W. O. L., & Yoneyama, T. (2018). Predictive maintenance optimization for aircraft redundant systems subjected to multiple wear profiles. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1654-1658.
Wang, L., Chen, Y., Zhao, X., & Xiang, J. (2024). Predictive maintenance scheduling for aircraft engines based on remaining useful life prediction. IEEE Internet of Things Journal, vol. 11(13), pp. 22156-22168.
Boeing. (2017). Boeing Highlights Analytics Capability with Customer Orders, New Name. https://boeing.mediaroom.com/2017-06-19-Boeing-Highlights-Analytics-Capability-with-Customer-Orders-New-Name
Brunton, S. L., Nathan Kutz, J., Manohar, K., Aravkin, A. Y., Morgansen, K., Klemisch, J., Goebel, N., Buttrick, J., Poskin, J., Blom-Schieber, A. W., Hogan, T., & McDonald, M. (2021). Data-driven aerospace engineering: reframing the industry with machine learning. AIAA Journal, vol. 59(8), pp. 2820-2847.
Clark, P. (2017). Buying the Big Jets Fleet Planning for Airlines. Third Edition. Routledge.
Collins Aerospace. (2021). Understanding the Impact of Aircraft Information Data From New Generation Aircraft on the ACARS Network, White Paper.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.
Hölzel, N. B., & Gollnick, V. (2015). Cost-benefit analysis of prognostics and condition-based maintenance concepts for commercial aircraft considering prognostic errors. Annual Conference of the Prognostics and Health Management Society.
Hu, Y., Miao, X., Si, Y., Pan, E., & Zio, E. (2022). Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering & System Safety, vol. 217, pp. 108063.
Hussain, Y. M., Burrow, S., Henson, L., & Keogh, P. (2016). Benefits analysis of prognostics & health monitoring to aircraft maintenance using system dynamics. European Conference of the Prognostics and Health Management Society.
IATA. (2022a). Aircraft Operational Availability (2nd ed.). https://www.iata.org/contentassets/bf8ca67c8bcd4358b3d004b0d6d0916f/aoa-2nded-2022.pdf
IATA. (2022b). IATA Ground Damage Report the case for enhanced ground support equipment. https://www.iata.org/en/programs/ops-infra/ground-operations/ground-damage-report/
IATA. (2023). From Aircraft Health Monitoring to Aircraft Health Management White Paper on AHM – Position Update. https://www.iata.org/en/programs/ops-infra/techops/mctg/
International Aircraft Development Fund [IADF]. (2014). History and mechanism of AHM, and Boeing 787 AHM. Kaisetsu Gaiyo, vol. 26(6). (in Japanese)
International Standards Organization (ISO). (2011). ISO/IEC/IEEE 42010: Systems and software engineering – Architecture Description.
Ito, K. (2013). Optimal maintenance policy for civil aircraft. REAJ Journal, vol. 35(1), pp. 18-23. (in Japanese)
Japan Aircraft Development Corporation [JADC]. (2024). Market forecast for commercial aircraft 2025-2044. YGR-5145. (in Japanese)
Jensen, G., Dan, E. E., Kihlström, L.-O., Altamiranda, E., & Hause, M. (2024). Model-based maintenance planning and analytics for oil & gas offshore systems. 35th Annual INCOSE International Symposium.
Koizumi, T. (2023). Development method of maintenance program in commercial aircraft development. Journal of the Japan Society for Aeronautical and Space Sciences, vol. 71(4), pp. 91-98. (in Japanese)
Koizumi, T., & Kogiso, N. (2024a). An effectiveness evaluation method using system of systems architecture description of aircraft health management in aircraft maintenance program. International Journal of Prognostics and Health Management, vol. 15(3), pp. 3837.
Koizumi, T., & Kogiso, N. (2024b). Trade-off analysis from system of systems perspective for AHM adoption to civil aircraft maintenance. Proceedings of the 34th Design Engineering and Systems Conference of JSME, 1409. (in Japanese with English abstract)
Koizumi, T., Morino, H., Hara, T., & Aoyama, K. (2025). Stakeholder harmonization of hydrogen supply business: An UAF approach to system dynamics in enterprise architecture and product service systems. 35th Annual INCOSE International Symposium.
Kordestani, M., Orchard, M. E., Khorasani, K., & Saif, M. (2023). An overview of the state of the art in aircraft prognostic and health management strategies. IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-15.
Kunche, S., Chen, C., & Pecht, M. (2012). A review of PHM system's architectural frameworks. The 54th Meeting of the Society for Machinery Failure Prevention Technology.
Levii Inc. (2023). Balus. https://levii.co.jp/services/balus
Liu, F., & Huang, Z. (2015). System dynamics based simulation approach on corrective maintenance cost of aviation equipments. Procedia Engineering, vol. 99, pp. 150-155.
Martin, J. N. (2025). Enabling enterprise transformation using system principles and concepts. 35th Annual INCOSE International Symposium.
Meissner, R., Meyer, H., & Wicke, K. (2021). Concept and economic evaluation of prescriptive maintenance strategies for an automated condition monitoring system. International Journal of Prognostics and Health Management, vol. 12(3).
Ministry of Land, Infrastructure, Transport and Tourism [MLIT]. (2025). Final report of the study group on securing and utilizing aircraft maintenance engineers and pilots. https://www.mlit.go.jp/koku/koku_tk5_000146.html (in Japanese)
MIT. (n.d.). Global Airline Industry Program. https://web.mit.edu/airlines/
Miura, M., & Sakamoto, Y. (2023). Interactive system modeling for designing a new concept launch vehicle. Aerospace Europe Conference 2023 – 10th EUCASS - 9th CEAS.
Olsson, E., Funk, P., Candell, O., Sohlberg, R., & Karim, R. (2021). Enterprise modeling for dynamic matching of tactical needs and aircraft maintenance capabilities. 15th International Conference and Workshop on Industrial AI.
Rodrigues, L. R., Gomes, J. P. P., Ferri, F. A. S., Medeiros, I. P., Galvão, R. K. H., & Nascimento Júnior, C. L. (2015). Use of PHM information and system architecture for optimized aircraft maintenance planning. IEEE Systems Journal, vol. 9(4), pp. 1197-1207.
Sriram, C., & Haghani, A. (2003). An optimization model for aircraft maintenance scheduling and re-assignment. Transportation Research Part A: Policy and Practice, vol. 37(1), pp. 29-48.
Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston, MA: Irwin/McGraw-Hill.
Teubert, C., Pohya, A. A., & Gorospe, G. (2023). Analysis of barriers preventing the widespread adoption of predictive and prescriptive maintenance in aviation. NASA TM–20230000841.
The Object Management Group [OMG]. (2022). Enterprise architecture guide for UAF (Informative). Formal/22-07-10. https://www.omg.org/spec/UAF/1.2
Ueda, Y., & Hidaka, K. (2024). A study of homogeneity and heterogeneity in maintenance costs for commercial aircraft. Transactions of the Japan Society for Aeronautical and Space Sciences, vol. 72(2), pp. 41-47. (in Japanese with English abstract)
U.S. Department of Transportation. (2024). Bureau of Transportation Statistics. https://www.transtats.bts.gov/homepage.asp
Verhoeff, M., Verhagen, W. J. C., & Curran, R. (2015). Maximizing operational readiness in military aviation by optimizing flight and maintenance planning. Transportation Research Procedia, vol. 10, pp. 941-950.
Vianna, W. O. L., & Yoneyama, T. (2018). Predictive maintenance optimization for aircraft redundant systems subjected to multiple wear profiles. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1654-1658.
Wang, L., Chen, Y., Zhao, X., & Xiang, J. (2024). Predictive maintenance scheduling for aircraft engines based on remaining useful life prediction. IEEE Internet of Things Journal, vol. 11(13), pp. 22156-22168.
Section
Regular Session Papers
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