A Conceptual Framework to Integrate Prognostics and Health Management with Maintenance, Repair and Overhaul for a Hydrogen-Electric Aircraft Propulsion System
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Jul 3, 2026
Lisandro A. Jimenez-Roa
Nela Koubkova Lothar Kerschgens Arjan de Jong
https://orcid.org/0000-0003-3062-8408
Nela Koubkova Lothar Kerschgens Arjan de Jong
Abstract
This paper proposes a conceptual integration framework that maps Prognostics and Health Management (PHM) stages onto the Maintenance, Repair and Overhaul (MRO) process chain, motivated by a use case on Hydrogen-Electric Aircraft Propulsion System (HEAPS). Drawing on scientific literature, standards, and domain expertise, we identify failure modes, mechanisms, and sensor measurands for HEAPS, and examine maintenance considerations through PHM and MRO lenses. The framework reveals a fundamental asymmetry. From PHM to MRO, diagnostics is the only stage with operational links, feeding condition-based updates to the Aircraft Maintenance Programme (AMP) without altering its structure; prognostic capabilities show potential for planning the unscheduled at the Part 145 level, but beyond diagnostics PHM outputs remain underutilised due to missing certification pathways and because the schedule-driven AMP revision cycle can be slower than the dynamic decision-making advanced PHM promises. From MRO to PHM, Part 145 provides the ground-truth feedback essential to train and validate PHM models, yet structured end-to-end feedback pipelines remain a challenge in scale and architecture.
How to Cite
Jimenez-Roa, L. A., Koubkova, N., Kerschgens, L., & de Jong, A. (2026). A Conceptual Framework to Integrate Prognostics and Health Management with Maintenance, Repair and Overhaul for a Hydrogen-Electric Aircraft Propulsion System. PHM Society European Conference, 9(1), 1–15. https://doi.org/10.36001/phme.2026.v9i1.4904
##plugins.themes.bootstrap3.article.details##
Keywords
Prognostics and Health Management (PHM), Maintenance, Repair and Overhaul (MRO), Hydrogen-Electric Aircraft Propulsion System (HEAPS)
References
Abbate, R., Franciosi, C., Voisin, A., & Fera, M. (2024). A conceptual framework proposal for the implementation of prognostic and health management in production systems. IET Collaborative Intelligent Manufacturing, 6(4). doi: 10.1049/cim2.12122
Abiru, Y., Nishiguchi, H., Maekawa, M., Nagata, T., Itaya, T., Koga, M., & Nishi, T. (2024). Hydrogen embrittlement detection technology using nondestructive testing for realizing a hydrogen society. Materials, 17(17). doi: 10.3390/ma17174237
Airlines for America. (2022). MSG-3: Operator/manufacturer scheduled maintenance development, Volume 1: Fixed wing aircraft. Revision 2022.1. Accessed: May 8, 2026.
Amaro, R. L., White, R. M., Looney, C. P., Drexler, E. S., & Slifka, A. J. (2018). Development of a model for hydrogen-assisted fatigue crack growth of pipeline steel. Journal of Pressure Vessel Technology, 140(2). doi: 10.1115/1.4038824
Belkacem, T., Ioualalen, A., Ribeiro, S., Rodriguez, N., Rouffet, J.-B., Bascans, J., & Signé, Q. (2024). EASA research — Machine learning application approval (MLEAP) final report (Tech. Rep. No. EASA.2021.C38). Cologne, Germany: European Union Aviation Safety Agency.
Benedettini, O., Baines, T., Lightfoot, H., & Greenough, R. (2009). State-of-the-art in integrated vehicle health management. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223(2), 157–170. doi: 10.1243/09544100JAERO446
Borghei, M., & Ghassemi, M. (2021). Insulation materials and systems for more- and all-electric aircraft: A review identifying challenges and future research needs. IEEE Transactions on Transportation Electrification, 7(3), 1930–1953. doi: 10.1109/TTE.2021.3050269
Campari, A., Ustolin, F., Alvaro, A., & Paltrinieri, N. (2023). A review on hydrogen embrittlement and risk-based inspection of hydrogen technologies. International Journal of Hydrogen Energy, 48(90), 35316–35346. doi: 10.1016/j.ijhydene.2023.05.293
Candell, O., Karim, R., & Söderholm, P. (2009). eMaintenance: Information logistics for maintenance support. Robotics and Computer-Integrated Manufacturing, 25(6), 937–944. doi: 10.1016/j.rcim.2009.04.005
Carvalho, T., Soares, F., Vita, R., Francisco, R., Basto, J., & Alcalá, S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137. doi: 10.1016/j.cie.2019.106024
Cerrada, M., Sánchez, R.-V., Li, C., Pacheco, F., Cabrera, D., Valente de Oliveira, J., & Vásquez, R. (2018). A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169–196. doi: 10.1016/j.ymssp.2017.06.012
Chen, H., Zhan, Z., Jiang, P., Sun, Y., Liao, L., Wan, X., ... Pan, M. (2022). Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA. Applied Energy, 310. doi: 10.1016/j.apenergy.2022.118556
Dahmene, F., Yaacoubi, S., El Mountassir, M., Langlois, C., & Bardoux, O. (2016). Towards efficient acoustic emission testing of COPV, without Felicity ratio criterion, during hydrogen filling. International Journal of Hydrogen Energy, 41(2), 1359–1368. doi: 10.1016/j.ijhydene.2015.11.065
Darli Rodrigues Vieira, P. L. L. (2016). Maintenance, repair and overhaul (MRO) fundamentals and strategies: An aeronautical industry overview. International Journal of Computer Applications, 135(12), 21–29. doi: 10.5120/ijca2016908563
Dijoux, E., Steiner, N., Benne, M., Péra, M.-C., & Grondin-Perez, B. (2022). Experimental validation of an active fault tolerant control strategy applied to a proton exchange membrane fuel cell. Electrochem, 3(4), 633–652. doi: 10.3390/electrochem3040042
EASA. (2023). Artificial intelligence roadmap 2.0: Human-centric approach to AI in aviation. Cologne, Germany: European Union Aviation Safety Agency.
e Oliveira, E., Miguéis, V., & Borges, J. (2023). Automatic root cause analysis in manufacturing: An overview and conceptualization. Journal of Intelligent Manufacturing, 34(5), 2061–2078. doi: 10.1007/s10845-022-01914-3
Esposito, M., Lazoi, M., Margarito, A., & Quarta, L. (2019). Innovating the maintenance repair and overhaul phase through digitalization. Aerospace, 6(5). doi: 10.3390/AEROSPACE6050053
European Commission. (2024). HEROPS: Hydrogen-electric zero emission propulsion system. Horizon Europe Innovation Action, Grant Agreement No. 101140499. doi: 10.3030/101140499
Fard, M., He, J., Huang, H., & Cao, Y. (2022). Aircraft distributed electric propulsion technologies: A review. IEEE Transactions on Transportation Electrification, 8(4), 4067–4090. doi: 10.1109/TTE.2022.3197332
Fu, S., & Avdelidis, N. (2023). Prognostic and health management of critical aircraft systems and components: An overview. Sensors, 23(19). doi: 10.3390/s23198124
Garg, A., & Deshmukh, S. (2006). Maintenance management: Literature review and directions. Journal of Quality in Maintenance Engineering, 12(3), 205–238. doi: 10.1108/13552510610685075
Gibey, G., Pahon, E., Zerhouni, N., & Hissel, D. (2025). Predictive maintenance of proton-exchange-membrane fuel cells for transportation applications. Energies, 18(11). doi: 10.3390/en18112957
Goebel, K., Daigle, M., Saxena, A., Sankararaman, S., Roychoudhury, I., & Celaya, J. (2017). Prognostics: The science of making predictions. CreateSpace Independent Publishing Platform. ISBN: 978-1539074830
Gullo, L. J., & Sallade, R. (2026). Development of the new IEEE recommended practice for prognostics and health management. IEEE Reliability Magazine, 1–5. doi: 10.1109/MRL.2026.3683631
Hallensleben, C., Albers, S., & Dewulf, W. (2025). Strategic bottlenecks in commercial aviation MRO: OEM influence and the response of traditional maintenance providers. Journal of the Air Transport Research Society, 5. doi: 10.1016/j.jatrs.2025.100084
Hendricks, C., Williard, N., Mathew, S., & Pecht, M. (2015). A failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries. Journal of Power Sources, 297, 113–120. doi: 10.1016/j.jpowsour.2015.07.100
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, 217. doi: 10.1016/j.ress.2021.108063
IEEE. (2017). IEEE standard framework for prognostics and health management of electronic systems. IEEE Std 1856-2017, 1–31. doi: 10.1109/IEEESTD.2017.8227036
ISO. (2003). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 1: General guidelines. ISO 13374-1:2003.
ISO. (2007). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 2: Data processing. ISO 13374-2:2007.
ISO. (2012). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 3: Communication. ISO 13374-3:2012.
ISO. (2015). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 4: Presentation. ISO 13374-4:2015.
ISO. (2018). Condition monitoring and diagnostics of machines — General guidelines. ISO 17359:2018.
ISO. (2025). Condition monitoring and diagnostics of machine systems — Prognostics — Part 1: General guidelines and requirements. ISO 13381-1:2025.
Jardine, A., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. doi: 10.1016/j.ymssp.2005.09.012
Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C., & Zerhouni, N. (2013). Prognostics and health management of PEMFC: State of the art and remaining challenges. International Journal of Hydrogen Energy, 38(35), 15307–15317. doi: 10.1016/j.ijhydene.2013.09.051
Kang, D., Kang, S., Kim, B., & Lee, K. (2025). Condition-based fatigue life monitoring of a high-pressure hydrogen storage vessel using a reduced basis digital twin. Engineering Structures, 336. doi: 10.1016/j.engstruct.2025.120196
Karapanagiotis, C., Breithaupt, M., Duffner, E., & Schukar, M. (2025). Real-time monitoring of hydrogen composite pressure vessels using surface-applied distributed fiber optic sensors. JPhys Photonics, 7(2). doi: 10.1088/2515-7647/adb9ac
Keith, B., O’Leary-Roseberry, T., Sanderse, B., Scheichl, R., & Van Bloemen Waanders, B. (2025). Scientific machine learning: A symbiosis. Foundations of Data Science, 7(1). doi: 10.3934/fods.2024051
Kim, K., Kim, J., Choi, H., Kwon, O., Jang, Y., Ryu, S., ... Cha, S. (2023). Pre-diagnosis of flooding and drying in proton exchange membrane fuel cells by bagging ensemble deep learning models using long short-term memory and convolutional neural networks. Energy, 266. doi: 10.1016/j.energy.2022.126441
Lance, G., Leroy, T., & Sery, J. (2024). Adaptive extended Kalman filter for PEMFC membrane water content estimation. International Journal of Hydrogen Energy, 71, 1164–1173. doi: 10.1016/j.ijhydene.2024.05.199
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. doi: 10.1016/j.mfglet.2014.12.001
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems: Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334. doi: 10.1016/j.ymssp.2013.06.004
Li, Z., Yu, J., Huang, Z., & Huang, Y. (2026). Surveying root cause analysis techniques: A comprehensive review of aspects for multi-service applications. IEEE Transactions on Services Computing, 19(1), 808–825. doi: 10.1109/TSC.2025.3631913
Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194). doi: 10.1098/rsta.2020.0209
Lim, I., Park, J., Choi, E., & Kim, M. (2021). Efficient fault diagnosis method of PEMFC thermal management system for various current densities. International Journal of Hydrogen Energy, 46(2), 2543–2554. doi: 10.1016/j.ijhydene.2020.10.085
Liu, H., Chen, J., Hissel, D., Lu, J., Hou, M., & Shao, Z. (2020). Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review. Renewable and Sustainable Energy Reviews, 123. doi: 10.1016/j.rser.2020.109721
Liu, J., Luo, W., Yang, X., & Wu, L. (2016). Robust model-based fault diagnosis for PEM fuel cell air-feed system. IEEE Transactions on Industrial Electronics, 63(5), 3261–3270. doi: 10.1109/TIE.2016.2535118
Long, B., Wu, K., Li, P., & Li, M. (2022). A novel remaining useful life prediction method for hydrogen fuel cells based on the gated recurrent unit neural network. Applied Sciences, 12(1). doi: 10.3390/app12010432
Mahmoodzadeh, Z., Wu, K.-Y., Droguett, E., & Mosleh, A. (2020). Condition-based maintenance with reinforcement learning for dry gas pipeline subject to internal corrosion. Sensors, 20(19), 1–26. doi: 10.3390/s20195708
Massaro, M., Biga, R., Kolisnichenko, A., Marocco, P., Monteverde, A., & Santarelli, M. (2023). Potential and technical challenges of on-board hydrogen storage technologies coupled with fuel cell systems for aircraft electrification. Journal of Power Sources, 555. doi: 10.1016/j.jpowsour.2022.232397
Mellal, M., Zio, E., & Pecht, M. (2023). Multi-objective reliability and cost optimization of fuel cell vehicle system with fuzzy feasibility. Information Sciences, 640. doi: 10.1016/j.ins.2023.119112
Meng, H., Liang, J., Hu, M., Salehi, F., & Han, T. (2026). A review on machine learning-based prognostics and health management for hydrogen fuel cells. Journal of Loss Prevention in the Process Industries, 99. doi: 10.1016/j.jlp.2025.105833
Miller, M. (2019). Failure mode and effects analysis on PEM fuel cell systems for aircraft power applications (Tech. Rep. No. DOT/FAA/TC-18/49). Atlantic City International Airport, NJ: Federal Aviation Administration, William J. Hughes Technical Center.
MIMOSA. (2006). Open system architecture for condition based maintenance (OSA-CBM).
Oh, H., Han, B., McCluskey, P., Han, C., & Youn, B. (2015). Physics-of-failure, condition monitoring, and prognostics of insulated gate bipolar transistor modules: A review. IEEE Transactions on Power Electronics, 30(5), 2413–2426. doi: 10.1109/TPEL.2014.2346485
Oh, H., Lee, W.-Y., Won, J., Kim, M., Choi, Y.-Y., & Han, S.-B. (2020). Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells. Applied Energy, 277. doi: 10.1016/j.apenergy.2020.115568
Orošnjak, M., Saretzky, F., & Kedziora, S. (2025). Prescriptive maintenance: A systematic literature review and exploratory meta-synthesis. Applied Sciences, 15(15). doi: 10.3390/app15158507
Parida, A., & Kumar, U. (2006). Maintenance performance measurement (MPM): Issues and challenges. Journal of Quality in Maintenance Engineering, 12(3), 239–251. doi: 10.1108/13552510610685084
Pecht, M., & Gu, J. (2009). Physics-of-failure-based prognostics for electronic products. Transactions of the Institute of Measurement and Control, 31(3–4), 309–322. doi: 10.1177/0142331208092031
Pettorossi, C., Morvillier, R., Heiries, V., Rosini, S., & Gerard, M. (2025). Physics-guided fault diagnosis method for proton exchange membrane fuel cells based on LSTM neural network. Journal of Power Sources, 626. doi: 10.1016/j.jpowsour.2024.235696
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in Industry 4.0. Reliability Engineering & System Safety, 234. doi: 10.1016/j.ress.2023.109204
Roijers, D., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67–113. doi: 10.1613/jair.3987
Ruijters, E., & Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 15, 29–62. doi: 10.1016/j.cosrev.2015.03.001
Scott, M., Verhagen, W., Bieber, M., & Marzocca, P. (2022). A systematic literature review of predictive maintenance for defence fixed-wing aircraft sustainment and operations. Sensors, 22(18). doi: 10.3390/s22187070
Sicilia, M., Cervone, D., Polverino, P., & Pianese, C. (2024). Advancements on lumped modelling of membrane water content for real-time prognostics and control of PEMFC. Energies, 17(19). doi: 10.3390/en17194841
Silveira, N., Meghoe, A., & Tinga, T. (2024). Quantifying the suitability and feasibility of predictive maintenance approaches. Computers & Industrial Engineering, 194. doi: 10.1016/j.cie.2024.110342
Sprong, J., Jiang, X., & Polinder, H. (2019). A deployment of prognostics to optimize aircraft maintenance: A literature review. In Proceedings of the Annual Conference of the Prognostics and Health Management Society (Vol. 11). doi: 10.36001/phmconf.2019.v11i1.776
Taghizad-Tavana, K., Ghanbari-Ghalehjoughi, M., Safari, A., Hagh, M., & Nezhad, A. (2025). From green hydrogen production to artificial intelligence-driven energy management in hydrogen fuel cell electric vehicles: A comprehensive review of technologies, optimization techniques, international standards, and investment programs. Applied Energy, 399. doi: 10.1016/j.apenergy.2025.126534
Tao, F., Zhang, M., Liu, Y., & Nee, A. (2018). Digital twin-driven prognostics and health management for complex equipment. CIRP Annals, 67(1), 169–172. doi: 10.1016/j.cirp.2018.04.055
Tiddens, W., Braaksma, J., & Tinga, T. (2023). Decision framework. Applied Sciences, 13(3). doi: 10.3390/app13032021
Tiwari, S., Pekris, M., & Doherty, J. (2024). A review of liquid hydrogen aircraft and propulsion technologies. International Journal of Hydrogen Energy, 57, 1174–1196. doi: 10.1016/j.ijhydene.2023.12.263
Tsallis, C., Papageorgas, P., Munteanu, R., & Dellagi, S. (2026). Large language and foundation models for machinery health monitoring: A systematic review. Applied Sciences, 16(5). doi: 10.3390/app16052493
Ucar, A., Karakose, M., & Kırımça, N. (2024). Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends. Applied Sciences, 14(2). doi: 10.3390/app14020898
Uhlmann, E., Bilz, M., & Baumgarten, J. (2013). MRO: Challenge and chance for sustainable enterprises. Procedia CIRP, 11, 239–244. doi: https://doi.org/10.1016/j.procir.2013.07.036
Vellaiyan, S., Aljohani, K., Aljohani, B., Kuppusamy, S., & Minh, N. (2026). A comprehensive review of AI-enabled predictive maintenance for hydrogen-powered transport: Advancing process safety and sustainability. Journal of Loss Prevention in the Process Industries, 101. doi: 10.1016/j.jlp.2026.105953
Verhagen, W., Santos, B., Freeman, F., van Kessel, P., Zarouchas, D., Loutas, T., ... Heiets, I. (2023). Condition-based maintenance in aviation: Challenges and opportunities. Aerospace, 10(9). doi: 10.3390/aerospace10090762
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards for prognostics and health management (PHM) techniques within manufacturing operations. In Proceedings of the Annual Conference of the Prognostics and Health Management Society (pp. 576–588). doi: 10.36001/phmconf.2014.v6i1.2503
Wang, J., Yang, B., Zeng, C., Chen, Y., Guo, Z., Li, D., ... Yu, T. (2021). Recent advances and summarization of fault diagnosis techniques for proton exchange membrane fuel cell systems: A critical overview. Journal of Power Sources, 500. doi: 10.1016/j.jpowsour.2021.229932
Weiss, M., Kamtsiuris, A., Raddatz, F., & Wende, G. (2025). Advanced MRO processes in Industry 4.0 with proactive Asset Administration Shell and Digital Product Passport. International Journal of Prognostics and Health Management, 16(2). doi: 10.36001/ijphm.2025.v16i2.4508
Whiteley, M., Dunnett, S., & Jackson, L. (2016). Failure mode and effect analysis, and fault tree analysis of polymer electrolyte membrane fuel cells. International Journal of Hydrogen Energy, 41(2), 1187–1202. doi: 10.1016/j.ijhydene.2015.11.007
Witzig, M., et al. (2023). AVIATAR: Optimizing airline flight operations with predictive health analytics and building on digital platform technology. PHM Society Asia-Pacific Conference, 4(1). doi: 10.36001/phmap.2023.v4i1.3775
Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018). Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering & System Safety, 178, 255–268. doi: 10.1016/j.ress.2018.06.021
Xiao, R., Hu, Q., & Li, J. (2019). Leak detection of gas pipelines using acoustic signals based on wavelet transform and support vector machine. Measurement, 146, 479–489. doi: 10.1016/j.measurement.2019.06.050
Yang, D., Wang, Y., & Chen, Z. (2020). Robust fault diagnosis and fault tolerant control for PEMFC system based on an augmented LPV observer. International Journal of Hydrogen Energy, 45(24), 13508–13522. doi: 10.1016/j.ijhydene.2020.03.063
Zhang, C., Zhang, Y., Wang, L., Deng, X., Liu, Y., & Zhang, J. (2023). A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures. Renewable and Sustainable Energy Reviews, 182. doi: 10.1016/j.rser.2023.113369
Zhang, T., Hou, Z., Li, X., Chen, Q., Wang, Q., Lüddeke, C., ... Sun, W. (2025). A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network. Applied Energy, 385. doi: 10.1016/j.apenergy.2025.125569
Zhang, X., & Pisu, P. (2014). Prognostic-oriented fuel cell catalyst aging modeling and its application to health-monitoring and prognostics of a PEM fuel cell. International Journal of Prognostics and Health Management, 5(1). doi: 10.36001/ijphm.2014.v5i1.2203
Zhou, D., Wu, Y., Gao, F., Breaz, E., Ravey, A., & Miraoui, A. (2017). Degradation prediction of PEM fuel cell stack based on multiphysical aging model with particle filter approach. IEEE Transactions on Industry Applications, 53(4), 4041–4052. doi: 10.1109/TIA.2017.2680406
Zio, E. (2022). Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218. doi: 10.1016/j.ress.2021.108119
Zuo, J., Steiner, N., Li, Z., Cadet, C., Bérenguer, C., & Hissel, D. (2025). Reinforcement learning-based maintenance scheduling for a stochastic deteriorating fuel cell considering stack-to-stack heterogeneity. Reliability Engineering & System Safety, 256. doi: 10.1016/j.ress.2024.110700
Abiru, Y., Nishiguchi, H., Maekawa, M., Nagata, T., Itaya, T., Koga, M., & Nishi, T. (2024). Hydrogen embrittlement detection technology using nondestructive testing for realizing a hydrogen society. Materials, 17(17). doi: 10.3390/ma17174237
Airlines for America. (2022). MSG-3: Operator/manufacturer scheduled maintenance development, Volume 1: Fixed wing aircraft. Revision 2022.1. Accessed: May 8, 2026.
Amaro, R. L., White, R. M., Looney, C. P., Drexler, E. S., & Slifka, A. J. (2018). Development of a model for hydrogen-assisted fatigue crack growth of pipeline steel. Journal of Pressure Vessel Technology, 140(2). doi: 10.1115/1.4038824
Belkacem, T., Ioualalen, A., Ribeiro, S., Rodriguez, N., Rouffet, J.-B., Bascans, J., & Signé, Q. (2024). EASA research — Machine learning application approval (MLEAP) final report (Tech. Rep. No. EASA.2021.C38). Cologne, Germany: European Union Aviation Safety Agency.
Benedettini, O., Baines, T., Lightfoot, H., & Greenough, R. (2009). State-of-the-art in integrated vehicle health management. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223(2), 157–170. doi: 10.1243/09544100JAERO446
Borghei, M., & Ghassemi, M. (2021). Insulation materials and systems for more- and all-electric aircraft: A review identifying challenges and future research needs. IEEE Transactions on Transportation Electrification, 7(3), 1930–1953. doi: 10.1109/TTE.2021.3050269
Campari, A., Ustolin, F., Alvaro, A., & Paltrinieri, N. (2023). A review on hydrogen embrittlement and risk-based inspection of hydrogen technologies. International Journal of Hydrogen Energy, 48(90), 35316–35346. doi: 10.1016/j.ijhydene.2023.05.293
Candell, O., Karim, R., & Söderholm, P. (2009). eMaintenance: Information logistics for maintenance support. Robotics and Computer-Integrated Manufacturing, 25(6), 937–944. doi: 10.1016/j.rcim.2009.04.005
Carvalho, T., Soares, F., Vita, R., Francisco, R., Basto, J., & Alcalá, S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137. doi: 10.1016/j.cie.2019.106024
Cerrada, M., Sánchez, R.-V., Li, C., Pacheco, F., Cabrera, D., Valente de Oliveira, J., & Vásquez, R. (2018). A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169–196. doi: 10.1016/j.ymssp.2017.06.012
Chen, H., Zhan, Z., Jiang, P., Sun, Y., Liao, L., Wan, X., ... Pan, M. (2022). Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA. Applied Energy, 310. doi: 10.1016/j.apenergy.2022.118556
Dahmene, F., Yaacoubi, S., El Mountassir, M., Langlois, C., & Bardoux, O. (2016). Towards efficient acoustic emission testing of COPV, without Felicity ratio criterion, during hydrogen filling. International Journal of Hydrogen Energy, 41(2), 1359–1368. doi: 10.1016/j.ijhydene.2015.11.065
Darli Rodrigues Vieira, P. L. L. (2016). Maintenance, repair and overhaul (MRO) fundamentals and strategies: An aeronautical industry overview. International Journal of Computer Applications, 135(12), 21–29. doi: 10.5120/ijca2016908563
Dijoux, E., Steiner, N., Benne, M., Péra, M.-C., & Grondin-Perez, B. (2022). Experimental validation of an active fault tolerant control strategy applied to a proton exchange membrane fuel cell. Electrochem, 3(4), 633–652. doi: 10.3390/electrochem3040042
EASA. (2023). Artificial intelligence roadmap 2.0: Human-centric approach to AI in aviation. Cologne, Germany: European Union Aviation Safety Agency.
e Oliveira, E., Miguéis, V., & Borges, J. (2023). Automatic root cause analysis in manufacturing: An overview and conceptualization. Journal of Intelligent Manufacturing, 34(5), 2061–2078. doi: 10.1007/s10845-022-01914-3
Esposito, M., Lazoi, M., Margarito, A., & Quarta, L. (2019). Innovating the maintenance repair and overhaul phase through digitalization. Aerospace, 6(5). doi: 10.3390/AEROSPACE6050053
European Commission. (2024). HEROPS: Hydrogen-electric zero emission propulsion system. Horizon Europe Innovation Action, Grant Agreement No. 101140499. doi: 10.3030/101140499
Fard, M., He, J., Huang, H., & Cao, Y. (2022). Aircraft distributed electric propulsion technologies: A review. IEEE Transactions on Transportation Electrification, 8(4), 4067–4090. doi: 10.1109/TTE.2022.3197332
Fu, S., & Avdelidis, N. (2023). Prognostic and health management of critical aircraft systems and components: An overview. Sensors, 23(19). doi: 10.3390/s23198124
Garg, A., & Deshmukh, S. (2006). Maintenance management: Literature review and directions. Journal of Quality in Maintenance Engineering, 12(3), 205–238. doi: 10.1108/13552510610685075
Gibey, G., Pahon, E., Zerhouni, N., & Hissel, D. (2025). Predictive maintenance of proton-exchange-membrane fuel cells for transportation applications. Energies, 18(11). doi: 10.3390/en18112957
Goebel, K., Daigle, M., Saxena, A., Sankararaman, S., Roychoudhury, I., & Celaya, J. (2017). Prognostics: The science of making predictions. CreateSpace Independent Publishing Platform. ISBN: 978-1539074830
Gullo, L. J., & Sallade, R. (2026). Development of the new IEEE recommended practice for prognostics and health management. IEEE Reliability Magazine, 1–5. doi: 10.1109/MRL.2026.3683631
Hallensleben, C., Albers, S., & Dewulf, W. (2025). Strategic bottlenecks in commercial aviation MRO: OEM influence and the response of traditional maintenance providers. Journal of the Air Transport Research Society, 5. doi: 10.1016/j.jatrs.2025.100084
Hendricks, C., Williard, N., Mathew, S., & Pecht, M. (2015). A failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries. Journal of Power Sources, 297, 113–120. doi: 10.1016/j.jpowsour.2015.07.100
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, 217. doi: 10.1016/j.ress.2021.108063
IEEE. (2017). IEEE standard framework for prognostics and health management of electronic systems. IEEE Std 1856-2017, 1–31. doi: 10.1109/IEEESTD.2017.8227036
ISO. (2003). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 1: General guidelines. ISO 13374-1:2003.
ISO. (2007). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 2: Data processing. ISO 13374-2:2007.
ISO. (2012). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 3: Communication. ISO 13374-3:2012.
ISO. (2015). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 4: Presentation. ISO 13374-4:2015.
ISO. (2018). Condition monitoring and diagnostics of machines — General guidelines. ISO 17359:2018.
ISO. (2025). Condition monitoring and diagnostics of machine systems — Prognostics — Part 1: General guidelines and requirements. ISO 13381-1:2025.
Jardine, A., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. doi: 10.1016/j.ymssp.2005.09.012
Jouin, M., Gouriveau, R., Hissel, D., Péra, M.-C., & Zerhouni, N. (2013). Prognostics and health management of PEMFC: State of the art and remaining challenges. International Journal of Hydrogen Energy, 38(35), 15307–15317. doi: 10.1016/j.ijhydene.2013.09.051
Kang, D., Kang, S., Kim, B., & Lee, K. (2025). Condition-based fatigue life monitoring of a high-pressure hydrogen storage vessel using a reduced basis digital twin. Engineering Structures, 336. doi: 10.1016/j.engstruct.2025.120196
Karapanagiotis, C., Breithaupt, M., Duffner, E., & Schukar, M. (2025). Real-time monitoring of hydrogen composite pressure vessels using surface-applied distributed fiber optic sensors. JPhys Photonics, 7(2). doi: 10.1088/2515-7647/adb9ac
Keith, B., O’Leary-Roseberry, T., Sanderse, B., Scheichl, R., & Van Bloemen Waanders, B. (2025). Scientific machine learning: A symbiosis. Foundations of Data Science, 7(1). doi: 10.3934/fods.2024051
Kim, K., Kim, J., Choi, H., Kwon, O., Jang, Y., Ryu, S., ... Cha, S. (2023). Pre-diagnosis of flooding and drying in proton exchange membrane fuel cells by bagging ensemble deep learning models using long short-term memory and convolutional neural networks. Energy, 266. doi: 10.1016/j.energy.2022.126441
Lance, G., Leroy, T., & Sery, J. (2024). Adaptive extended Kalman filter for PEMFC membrane water content estimation. International Journal of Hydrogen Energy, 71, 1164–1173. doi: 10.1016/j.ijhydene.2024.05.199
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. doi: 10.1016/j.mfglet.2014.12.001
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems: Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334. doi: 10.1016/j.ymssp.2013.06.004
Li, Z., Yu, J., Huang, Z., & Huang, Y. (2026). Surveying root cause analysis techniques: A comprehensive review of aspects for multi-service applications. IEEE Transactions on Services Computing, 19(1), 808–825. doi: 10.1109/TSC.2025.3631913
Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194). doi: 10.1098/rsta.2020.0209
Lim, I., Park, J., Choi, E., & Kim, M. (2021). Efficient fault diagnosis method of PEMFC thermal management system for various current densities. International Journal of Hydrogen Energy, 46(2), 2543–2554. doi: 10.1016/j.ijhydene.2020.10.085
Liu, H., Chen, J., Hissel, D., Lu, J., Hou, M., & Shao, Z. (2020). Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review. Renewable and Sustainable Energy Reviews, 123. doi: 10.1016/j.rser.2020.109721
Liu, J., Luo, W., Yang, X., & Wu, L. (2016). Robust model-based fault diagnosis for PEM fuel cell air-feed system. IEEE Transactions on Industrial Electronics, 63(5), 3261–3270. doi: 10.1109/TIE.2016.2535118
Long, B., Wu, K., Li, P., & Li, M. (2022). A novel remaining useful life prediction method for hydrogen fuel cells based on the gated recurrent unit neural network. Applied Sciences, 12(1). doi: 10.3390/app12010432
Mahmoodzadeh, Z., Wu, K.-Y., Droguett, E., & Mosleh, A. (2020). Condition-based maintenance with reinforcement learning for dry gas pipeline subject to internal corrosion. Sensors, 20(19), 1–26. doi: 10.3390/s20195708
Massaro, M., Biga, R., Kolisnichenko, A., Marocco, P., Monteverde, A., & Santarelli, M. (2023). Potential and technical challenges of on-board hydrogen storage technologies coupled with fuel cell systems for aircraft electrification. Journal of Power Sources, 555. doi: 10.1016/j.jpowsour.2022.232397
Mellal, M., Zio, E., & Pecht, M. (2023). Multi-objective reliability and cost optimization of fuel cell vehicle system with fuzzy feasibility. Information Sciences, 640. doi: 10.1016/j.ins.2023.119112
Meng, H., Liang, J., Hu, M., Salehi, F., & Han, T. (2026). A review on machine learning-based prognostics and health management for hydrogen fuel cells. Journal of Loss Prevention in the Process Industries, 99. doi: 10.1016/j.jlp.2025.105833
Miller, M. (2019). Failure mode and effects analysis on PEM fuel cell systems for aircraft power applications (Tech. Rep. No. DOT/FAA/TC-18/49). Atlantic City International Airport, NJ: Federal Aviation Administration, William J. Hughes Technical Center.
MIMOSA. (2006). Open system architecture for condition based maintenance (OSA-CBM).
Oh, H., Han, B., McCluskey, P., Han, C., & Youn, B. (2015). Physics-of-failure, condition monitoring, and prognostics of insulated gate bipolar transistor modules: A review. IEEE Transactions on Power Electronics, 30(5), 2413–2426. doi: 10.1109/TPEL.2014.2346485
Oh, H., Lee, W.-Y., Won, J., Kim, M., Choi, Y.-Y., & Han, S.-B. (2020). Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells. Applied Energy, 277. doi: 10.1016/j.apenergy.2020.115568
Orošnjak, M., Saretzky, F., & Kedziora, S. (2025). Prescriptive maintenance: A systematic literature review and exploratory meta-synthesis. Applied Sciences, 15(15). doi: 10.3390/app15158507
Parida, A., & Kumar, U. (2006). Maintenance performance measurement (MPM): Issues and challenges. Journal of Quality in Maintenance Engineering, 12(3), 239–251. doi: 10.1108/13552510610685084
Pecht, M., & Gu, J. (2009). Physics-of-failure-based prognostics for electronic products. Transactions of the Institute of Measurement and Control, 31(3–4), 309–322. doi: 10.1177/0142331208092031
Pettorossi, C., Morvillier, R., Heiries, V., Rosini, S., & Gerard, M. (2025). Physics-guided fault diagnosis method for proton exchange membrane fuel cells based on LSTM neural network. Journal of Power Sources, 626. doi: 10.1016/j.jpowsour.2024.235696
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in Industry 4.0. Reliability Engineering & System Safety, 234. doi: 10.1016/j.ress.2023.109204
Roijers, D., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67–113. doi: 10.1613/jair.3987
Ruijters, E., & Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 15, 29–62. doi: 10.1016/j.cosrev.2015.03.001
Scott, M., Verhagen, W., Bieber, M., & Marzocca, P. (2022). A systematic literature review of predictive maintenance for defence fixed-wing aircraft sustainment and operations. Sensors, 22(18). doi: 10.3390/s22187070
Sicilia, M., Cervone, D., Polverino, P., & Pianese, C. (2024). Advancements on lumped modelling of membrane water content for real-time prognostics and control of PEMFC. Energies, 17(19). doi: 10.3390/en17194841
Silveira, N., Meghoe, A., & Tinga, T. (2024). Quantifying the suitability and feasibility of predictive maintenance approaches. Computers & Industrial Engineering, 194. doi: 10.1016/j.cie.2024.110342
Sprong, J., Jiang, X., & Polinder, H. (2019). A deployment of prognostics to optimize aircraft maintenance: A literature review. In Proceedings of the Annual Conference of the Prognostics and Health Management Society (Vol. 11). doi: 10.36001/phmconf.2019.v11i1.776
Taghizad-Tavana, K., Ghanbari-Ghalehjoughi, M., Safari, A., Hagh, M., & Nezhad, A. (2025). From green hydrogen production to artificial intelligence-driven energy management in hydrogen fuel cell electric vehicles: A comprehensive review of technologies, optimization techniques, international standards, and investment programs. Applied Energy, 399. doi: 10.1016/j.apenergy.2025.126534
Tao, F., Zhang, M., Liu, Y., & Nee, A. (2018). Digital twin-driven prognostics and health management for complex equipment. CIRP Annals, 67(1), 169–172. doi: 10.1016/j.cirp.2018.04.055
Tiddens, W., Braaksma, J., & Tinga, T. (2023). Decision framework. Applied Sciences, 13(3). doi: 10.3390/app13032021
Tiwari, S., Pekris, M., & Doherty, J. (2024). A review of liquid hydrogen aircraft and propulsion technologies. International Journal of Hydrogen Energy, 57, 1174–1196. doi: 10.1016/j.ijhydene.2023.12.263
Tsallis, C., Papageorgas, P., Munteanu, R., & Dellagi, S. (2026). Large language and foundation models for machinery health monitoring: A systematic review. Applied Sciences, 16(5). doi: 10.3390/app16052493
Ucar, A., Karakose, M., & Kırımça, N. (2024). Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends. Applied Sciences, 14(2). doi: 10.3390/app14020898
Uhlmann, E., Bilz, M., & Baumgarten, J. (2013). MRO: Challenge and chance for sustainable enterprises. Procedia CIRP, 11, 239–244. doi: https://doi.org/10.1016/j.procir.2013.07.036
Vellaiyan, S., Aljohani, K., Aljohani, B., Kuppusamy, S., & Minh, N. (2026). A comprehensive review of AI-enabled predictive maintenance for hydrogen-powered transport: Advancing process safety and sustainability. Journal of Loss Prevention in the Process Industries, 101. doi: 10.1016/j.jlp.2026.105953
Verhagen, W., Santos, B., Freeman, F., van Kessel, P., Zarouchas, D., Loutas, T., ... Heiets, I. (2023). Condition-based maintenance in aviation: Challenges and opportunities. Aerospace, 10(9). doi: 10.3390/aerospace10090762
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards for prognostics and health management (PHM) techniques within manufacturing operations. In Proceedings of the Annual Conference of the Prognostics and Health Management Society (pp. 576–588). doi: 10.36001/phmconf.2014.v6i1.2503
Wang, J., Yang, B., Zeng, C., Chen, Y., Guo, Z., Li, D., ... Yu, T. (2021). Recent advances and summarization of fault diagnosis techniques for proton exchange membrane fuel cell systems: A critical overview. Journal of Power Sources, 500. doi: 10.1016/j.jpowsour.2021.229932
Weiss, M., Kamtsiuris, A., Raddatz, F., & Wende, G. (2025). Advanced MRO processes in Industry 4.0 with proactive Asset Administration Shell and Digital Product Passport. International Journal of Prognostics and Health Management, 16(2). doi: 10.36001/ijphm.2025.v16i2.4508
Whiteley, M., Dunnett, S., & Jackson, L. (2016). Failure mode and effect analysis, and fault tree analysis of polymer electrolyte membrane fuel cells. International Journal of Hydrogen Energy, 41(2), 1187–1202. doi: 10.1016/j.ijhydene.2015.11.007
Witzig, M., et al. (2023). AVIATAR: Optimizing airline flight operations with predictive health analytics and building on digital platform technology. PHM Society Asia-Pacific Conference, 4(1). doi: 10.36001/phmap.2023.v4i1.3775
Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018). Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering & System Safety, 178, 255–268. doi: 10.1016/j.ress.2018.06.021
Xiao, R., Hu, Q., & Li, J. (2019). Leak detection of gas pipelines using acoustic signals based on wavelet transform and support vector machine. Measurement, 146, 479–489. doi: 10.1016/j.measurement.2019.06.050
Yang, D., Wang, Y., & Chen, Z. (2020). Robust fault diagnosis and fault tolerant control for PEMFC system based on an augmented LPV observer. International Journal of Hydrogen Energy, 45(24), 13508–13522. doi: 10.1016/j.ijhydene.2020.03.063
Zhang, C., Zhang, Y., Wang, L., Deng, X., Liu, Y., & Zhang, J. (2023). A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures. Renewable and Sustainable Energy Reviews, 182. doi: 10.1016/j.rser.2023.113369
Zhang, T., Hou, Z., Li, X., Chen, Q., Wang, Q., Lüddeke, C., ... Sun, W. (2025). A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network. Applied Energy, 385. doi: 10.1016/j.apenergy.2025.125569
Zhang, X., & Pisu, P. (2014). Prognostic-oriented fuel cell catalyst aging modeling and its application to health-monitoring and prognostics of a PEM fuel cell. International Journal of Prognostics and Health Management, 5(1). doi: 10.36001/ijphm.2014.v5i1.2203
Zhou, D., Wu, Y., Gao, F., Breaz, E., Ravey, A., & Miraoui, A. (2017). Degradation prediction of PEM fuel cell stack based on multiphysical aging model with particle filter approach. IEEE Transactions on Industry Applications, 53(4), 4041–4052. doi: 10.1109/TIA.2017.2680406
Zio, E. (2022). Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218. doi: 10.1016/j.ress.2021.108119
Zuo, J., Steiner, N., Li, Z., Cadet, C., Bérenguer, C., & Hissel, D. (2025). Reinforcement learning-based maintenance scheduling for a stochastic deteriorating fuel cell considering stack-to-stack heterogeneity. Reliability Engineering & System Safety, 256. doi: 10.1016/j.ress.2024.110700
Section
Technical Papers
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.