Health monitoring technologies and data analytics are increasingly widespread in the aviation industry following the growth in the capacity and speed of abundant and accurate data generation and transmission from the aircraft systems. These advances are fueling a change process in aircraft maintenance strategy towards a more proactive, precise, and effective approach consolidated in the concepts of Integrated Vehicle Health Monitoring (IVHM) and Prognostics and Health Management (PHM). Following that, several model-based and data-driven prognostics methods for Remaining Useful Life (RUL) estimation have been developed in the pursuit of improving predictive maintenance interventions for different types of components. Recent papers showcased the significant challenges faced to achieve forecast accuracy as posed by the inherent uncertainty involved in the functional dynamics of complex systems. This work acknowledges these difficulties and tackles variability by embracing it in the methodology deployed by means of considering in its framework the confidence intervals associated with the estimates for a predefined level of confidence.
Nevertheless, the ability to pinpoint times-to-failure by itself is arguably not enough to yield better operational results and improve support levels of service given that scattered standalone interventions may even cause failure occurrences and total downtime to increase. This study demonstrates the rationale behind those effects and exposes the necessity for a method for achieving a compromise to optimally accommodate the concurrent economic, reliability and maintainability goals which are, respectively, the maximization of component useful life expenditure, the minimization of the running-into-failure risk and the minimization of total downtime.
Further on, the article explores the problem in detail identifying the key parameters pointed out in the literature that need to be addressed by the modelling process to ensure the soundness of the method. The text then proposes a solution consisting of an innovative analytical model that optimizes operational availability through the dynamic allocation of flight-hours to each aircraft part of a fleet based on the integration of predictive and scheduled maintenance, minimizing total downtime, while accounting for prognostics uncertainty and the associated the risk of failure and incurring in corrective maintenance. The intended outcome is the capability of providing dynamic maintenance plans specially adjusted to each tail number according to its assessed health status and a calculated prognostic that considers predetermined future flights specifically attributed to optimize the overall availability of the fleet.
An illustrative case study involving multiple components with different aging parameters equipping the aircraft of a small military fleet operating from a single base was used to test the solution and produced results that corroborate the validity of the approach adopted and demonstrate the model’s value and effectiveness. The results also indicate there is significant potential to expand the study and encourage its further development to contemplate multiple-base scenarios and incorporate more detailed aspects such as tasks location within the aircraft, availability of spare parts and resources in general, out-of-phase items and its implementation together with a simulation tool to generalize its application.
The main contributions of the study are twofold. It adds on the theoretical complexity by tackling systems of systems instead of the predominant single component approach, and it provides a model with an optimizing objective function to improve maintenance planning in real-life.
How to Cite
Maintenance Modelling, Predictive Maintenance, PHM, Optimization, IVHM, Aviation, Military Aviation
 SAE International. ARP6275 - Determination of Cost Benefits from Implementing an Integrated Vehicle Health Management System 2019.
 de Jonge B, Scarf PA. A review on maintenance optimization. Eur J Oper Res 2019. https://doi.org/10.1016/j.ejor.2019.09.047.
 Vandawaker RM, Jacques DR, Freels JK. Impact of prognostic uncertainty in system health monitoring. Int J Progn Heal Manag 2015;6:1–13.
 Wilmering TJ, Ramesh A V. Assessing the impact of health management approaches on system total cost of ownership. IEEE Aerosp Conf Proc 2005;2005. https://doi.org/10.1109/AERO.2005.1559697.
 Shi Y, Zhu W, Xiang Y, Feng Q. Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement. Reliab Eng Syst Saf 2020;202. https://doi.org/10.1016/j.ress.2020.107042.
 Ahmadi A, Fransson T, Crona A, Klein M, Söderholm P. Integration of RCM and PHM for the next generation of aircraft. IEEE Aerosp Conf Proc 2009. https://doi.org/10.1109/AERO.2009.4839684.
 Li R, Verhagen WJC, Curran R. A systematic methodology for Prognostic and Health Management system architecture definition. Reliab Eng Syst Saf 2020;193:106598. https://doi.org/10.1016/j.ress.2019.106598.
 Bousdekis A, Magoutas B, Apostolou D, Mentzas G. A proactive decision making framework for condition-based maintenance. Ind Manag Data Syst 2015;115:1225–50. https://doi.org/10.1108/IMDS-03-2015-0071.
 Eliaz N, Latanision RM. Preventative maintenance and failure analysis of aircraft components. vol. 25. 2007. https://doi.org/10.1515/CORRREV.2007.25.1-2.107.
 Sudolsky MD. IVHM solutions using commercially-available aircraft condition monitoring systems. IEEE Aerosp. Conf. Proc., 2007. https://doi.org/10.1109/AERO.2007.352922.
 Baek JG. An intelligent condition-based maintenance scheduling model. Int J Qual Reliab Manag 2007. https://doi.org/10.1108/02656710710730898.
 Lv Z, Wang J, Zhang G, Jiayang H. Maintenance for Aircraft Engine Systems. 2015 IEEE Conf Progn Heal Manag 2015:1–6. https://doi.org/10.1109/ICPHM.2015.7245055.
 Peppard J. Building the Business Case for Integrated Vehicle Health Management 2010:100 M4-Citavi.
 Ferreiro S, Arnaiz A, Sierra B, Irigoien I. Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept. Expert Syst Appl 2012;39:6402–18. https://doi.org/10.1016/j.eswa.2011.12.027.
 Singh L, Singh PN, Srivastava PA. Integrated Vehicle Health Management System for Fighter Aircraft 2016;2:120–5.
 Grenyer A, Dinmohammadi F, Erkoyuncu JA, Zhao Y, Roy R. Current practice and challenges towards handling uncertainty for effective outcomes in maintenance. Procedia CIRP 2020;86:282–7. https://doi.org/10.1016/j.procir.2020.01.024.
 Adhikari PP, Buderath M. A Framework for Aircraft Maintenance Strategy including CBM. Eur Conf Progn Heal Manag Soc 2016 2016:1–10.
 Esperon-Miguez M, John P, Jennions IK. A review of Integrated Vehicle Health Management tools for legacy platforms: Challenges and opportunities. Prog Aerosp Sci 2013;56:19–34. https://doi.org/10.1016/j.paerosci.2012.04.003.
 Hölzel NB, Schröder C, Schilling T, Gollnick V. A Maintenance Packaging and Scheduling Optimization Method for Future Aircraft 2012:343–53.
 Deng Q, Santos BF, Curran R. A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization. Eur J Oper Res 2020;281:256–73. https://doi.org/10.1016/j.ejor.2019.08.025.
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