Skip to main content
SpringerLink
Log in

Advanced Data Analytics and Digital Technologies for Smart and Sustainable Maintenance

  • Conference paper
  • First Online:
Solutions for Maintenance Repair and Overhaul (ISATECH 2021)

Part of the book series: Sustainable Aviation ((SA))

Included in the following conference series:

  • 120 Accesses

Abstract

Maintenance optimization has been of high interest in recent years for both the industry and the knowledge institutions. For example, tens of billions of dollars are spent on annual aviation maintenance, repair, and overhaul (MRO) activities. At the same time, the attention also grows in the direction of the advances in data analytics and digital technologies which can enable the next step in maintenance transition from preventive to predictive. The integration and operational deployment of physics-based (domain knowledge) and data-driven (AI, digital twin) innovative technologies can enhance the optimization of lifecycles and processes. Main objectives are the reduction of aircraft downtime and costs as well as a minimal waste in terms of materials and energy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Apostolidis, A., & Stamoulis, K. P. (2021). An AI-based digital twin case study in the MRO sector. Transportation Research Procedia, 56, 55–62.

    Article  Google Scholar 

  • Apostolidis, A., Pelt, M., & Stamoulis, K. P. (2020). Aviation data analytics in MRO operations: Prospects and pitfalls. In Proceedings of IEEE RAMS 2020, Palm Springs.

    Google Scholar 

  • Baptista, M., Sankararaman, S., de Medeiros, I. P., Nascimento, C., Jr., Prendinger, H., & Henriques, E. M. P. (2018). Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Computers and Industrial Engineering, 115, 41–53.

    Article  Google Scholar 

  • Daily, J., & Peterson, J. (2017). Predictive maintenance: How big data analysis can improve maintenance. In K. Richter & J. Walther (Eds.), Supply chain integration challenges in commercial aerospace. Springer.

    Google Scholar 

  • Deng, Q., Santos, B. F., & Curran, R. (2020). A practical dynamic programming-based methodology for aircraft maintenance check scheduling optimization. European Journal of Operational Research, 281(2).

    Google Scholar 

  • Glaessgen, E., & Stargel D. (2012). The digital twin paradigm for future NASA and US air force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, AIAA paper 2012–1818.

    Google Scholar 

  • Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann Publishers.

    MATH  Google Scholar 

  • IATA Maintenance Cost Technical Group. (2019). Airline maintenance cost executive commentary. Available online: https://www.iata.org/contentassets/bf8ca67c8bcd4358b3d004b0d6d0916f/mctg-fy2018-report-public.pdf

  • Li, C., Mahadevan, S., Ling, Y., Wang, L., & Choze, S. (2017). A dynamic Bayesian network approach for digital twin. In Proceedings of the 19th AIAA non-deterministic approaches conference, Grapevine.

    Google Scholar 

  • Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.

    Article  Google Scholar 

  • Liu, Z., Meyendorf, N., & Mrad, N. (2018). The role of data fusion in predictive maintenance using digital twin. AIP Conference Proceedings, 1949, 020023.

    Article  Google Scholar 

  • Nguyen, D. V., Kefalas, M., Limmer, S., Apostolidis, A., Yang, K., Olhofer, M., & Bäck, T. (2019). A review: Research and application of prognostics and health management in automotive and aerospace industries. International Journal of Prognostics and Health Management, 10(2).

    Google Scholar 

  • Nilsson, A., Smith, S., Ulm, G., Gustavsson, E., & Jirstrand, M. (2018). A performance evaluation of federated learning algorithms. In Proceedings of the 2nd workshop on distributed infrastructures for deep learning.

    Google Scholar 

  • Pelt, M., Apostolidis, A., de Boer, R. J., Borst, M., Broodbakker, J., Patron, R. F., Helwani, L., Jansen, R., & Stamoulis, K. P. (2019a). Data mining in MRO. Faculty of Technology, Amsterdam University of Applied Sciences.

    Google Scholar 

  • Pelt, M., Stamoulis, K., & Apostolidis, A. (2019b). Data analytics case studies in the maintenance, repair and overhaul (MRO) industry. MATEC Web of Conferences, 304, 04005.

    Article  Google Scholar 

  • Phillips, P., Diston, D., Starr, A., Payne, J., & Pandya, S. (2010). A review on the optimisation of aircraft maintenance with application to landing gears. In D. Kiritsis, C. Emmanouilidis, A. Koronios, & J. Mathew (Eds.), Engineering asset lifecycle management. Springer.

    Google Scholar 

  • Stamoulis, K. P., & Apostolidis, A. (2023). Diagnostics, prognostics & advanced maintenance technologies for aviation MRO. Springer. in preparation.

    Google Scholar 

  • Tinga, T., & Loendersloot, R. (2014). Aligning PHM, SHM and CBM by understanding the physical system failure behaviour. In Second European conference of the prognostics and health management society.

    Google Scholar 

  • Wright, L., & Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences, 7(13).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Technology, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Konstantinos P. Stamoulis

Authors
  1. Konstantinos P. Stamoulis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Konstantinos P. Stamoulis .

Editor information

Editors and Affiliations

  1. Faculty of Aeronautics and Astronautics, Eskisehir Technical University, Eskisehir, Türkiye

    T. Hikmet Karakoc

  2. Aeronautics and Naval Architecture, Budapest University of Technology and Economics, Budapest, Hungary

    József Rohács

  3. Aeronautics and Naval Architecture, Budapest University of Technology and Economics, Budapest, Hungary

    Dániel Rohács

  4. Aviation, Iğdır Üniversitesi, Igdir, Türkiye

    Selçuk Ekici

  5. School of Aviation, Süleyman Demirel University, Keciborlu, Isparta, Türkiye

    Alper Dalkiran

  6. Aeronautics and Naval Architecture, Budapest University of Technology and Economics, Budapest, Hungary

    Utku Kale

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stamoulis, K.P. (2024). Advanced Data Analytics and Digital Technologies for Smart and Sustainable Maintenance. In: Karakoc, T.H., Rohács, J., Rohács, D., Ekici, S., Dalkiran, A., Kale, U. (eds) Solutions for Maintenance Repair and Overhaul. ISATECH 2021. Sustainable Aviation. Springer, Cham. https://doi.org/10.1007/978-3-031-38446-2_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-38446-2_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-38445-5

  • Online ISBN: 978-3-031-38446-2

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation