Digital Twin and Its Application for the Maintenance of Aircraft

  • Reference work entry
  • First Online:
Handbook of Nondestructive Evaluation 4.0

Abstract

Digital twin is a generic term used across disciplines to mean a digital copy of a physical entity. Facilitated by the new generation of information technology, the digital twin has drawn more and more attention from both academia and industry. Especially, as predicted in many studies, this technique has great potential to bring innovative and revolutionary changes for aerospace. This chapter gives a general introduction of digital twin. Furthermore, its application for the maintenance, repair, and overhaul of aircraft is outlined. In this chapter, first of all, the concept of digital twin is introduced, including its generation, development, and general components. Then, the maintenance process for aircraft as well as the existing issues is described. After that, the digital twin with the application of aircraft maintenance is elaborated from the perspectives of concept, system architecture, and system implementation. Subsequently, three cases are presented to illustrate how this digital twin works in the health status evaluation, future health status prediction, and maintenance activity management. At last, the summary of this chapter is made, along with a review about the challenges faced for implementing digital twin technology in aerospace.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 748.99
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 791.24
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Grieves M. Digital twin: manufacturing excellence through virtual factory replication. White Paper. 2014;1:1–7.

    Google Scholar 

  2. Tao F, Meng Z. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access. 2017;5:20418–27.

    Article  Google Scholar 

  3. Zhuang CB, Liu JH, **ong H, Ding X, Liu S, Weng G. Connotation, architecture and trends of product digital twin. Comput Integr Manuf. 2017;23(4):753–68.

    Google Scholar 

  4. Weyer S, Meyer T, Ohmer M, Gorecky D, Zühlke D. Future modeling and simulation of CPS-based factories: an example from the automotive industry. IFAC-PapersOnLine. 2016;49(31):97–102.

    Article  Google Scholar 

  5. **song Y, Yue S, Tang Diyin A. Digital twin approach based on nonparametric Bayesian network for complex system health monitoring. J Manuf Syst. 2020; https://doi.org/10.1016/j.jmsy.2020.07.005.

  6. Hamad Al-kaabi AP, Naim M. An outsourcing decision model for airlines’ MRO activities. J Qual Maint Eng. 2007;13(3):217–27.

    Article  Google Scholar 

  7. Jalil D, Bakar S, Khir M, Fauzi M. Integrated facility platform for next-gen aircraft maintenance, repair and overhaul (MRO). Int J Comput Sci Inf Secur. 2017;15(5):356–62.

    Google Scholar 

  8. Maurice P. Data mining for aircraft maintenance repair and overhaul (MRO). 2019. http://www.amsterdamuas.com/binaries/content/assets/subsites/aviation/data-miningin-mro/190417-data-mining-for-mro-presentation-final-s.pdf?1559025732768. Accessed 17 Apr 2019.

  9. Nadine E. How MRO is unlocking huge opportunities for digital twins in aviation. 2019. https://www.aviationtoday.com/2019/03/26/mro-unlocking-huge-opportunities-digitaltwins-aviation. Accessed 26 Mar 2019.

  10. Wang L, Ranjan R, Chen J, Benatallah B. Cloud computing: methodology, systems, and applications. London: CRC Press; 2017.

    Book  Google Scholar 

  11. Hugh R. MRO 4.0: The next big step for processes – using digital twins and location. 2019. http://www.aircraftit.com/articles/improving-business-efficiency-in-an-mro-environment. Accessed 15 Apr 2019.

  12. Iorga M, Feldman L, Barton R, Martin M, Goren N, Mahmoudi C. The nist definition of fog computing. No. NIST Special Publication (SP) 800–191 (Draft). Natl Inst Stand Technol. 2017; https://doi.org/10.6028/NIST.SP.500-325. Accessed 15 March 2018.

  13. Liao M, Guillaume R, Yan B. Airframe digital twin technology adaptability assessment and technology demonstration. Eng Fract Mech. 2020;225:1–15.

    Article  Google Scholar 

  14. Li C, Mahadevan S, Ling Y, Choze S, Wang L. Dynamic Bayesian network for aircraft wing health monitoring digital twin. AIAA J. 2017;55(3):930–41.

    Article  Google Scholar 

  15. Hua W, Hou J, Jiang Z. Fault diagnosis and prognostic methods based on hybrid system theory for MEHI systems in aircraft. Appl Mech Mater. 2013;389:550–5.

    Article  Google Scholar 

  16. Liu Z, Norbert M, Nezih M. The role of data fusion in predictive maintenance using digital twin. AIP Conf Proc. 2018;1949(1):1–6.

    Google Scholar 

  17. Millwater H, Juan O, Nathan C. Probabilistic methods for risk assessment of airframe digital twin structures. Eng Fract Mech. 2019;221:1–24.

    Article  Google Scholar 

  18. García C, Teresa E, Joseba Q. PHM techniques for condition-based maintenance based on hybrid system model representation. Proc Annu Conf Progn Health Manag Soc. 2010:1–8.

    Google Scholar 

  19. Carboni M, Stefano C. Advanced ultrasonic “probability of detection” curves for designing in-service inspection intervals. Int J Fatigue. 2016;86:77–87.

    Article  CAS  Google Scholar 

  20. Lei Y, Li N, Guo L, Li N, Yan T, Lin J. Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech Syst Signal Process. 2018;104:799–834.

    Article  Google Scholar 

  21. Lincoln J. Method for computation of structural failure probability for an aircraft. No. ASDTR- 80-5035. In: Aeronautical systems div wright-Patterson afb oh; 1980.

    Google Scholar 

  22. Glaessgen E. David S. the digital twin paradigm for future NASA and US air force vehicles. 14th AIAA Conf Proc. 2012:1–14.

    Google Scholar 

  23. Basri EI, Razak IHA, Ab-Samat H, Kamaruddin S. Preventive maintenance (PM) planning: a review. J Qual Maint Eng. 2017;23(2):114–43.

    Article  Google Scholar 

  24. Seshadri B, Thiagarajan K. Structural health management of damaged aircraft structures using digital twin concept. 25th AIAA Conf Proc. 2017;1–13

    Google Scholar 

  25. Paris P. Fazil E. a critical analysis of crack propagation laws. J Basic Eng. 1963;85(4):528–33.

    Article  CAS  Google Scholar 

  26. Cochran JK, Horng SM, Fowler JW. A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines. Comput Oper Res. 2003;30(7):1087–102.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zheng Liu .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Wang, T., Liu, Z. (2022). Digital Twin and Its Application for the Maintenance of Aircraft. In: Meyendorf, N., Ida, N., Singh, R., Vrana, J. (eds) Handbook of Nondestructive Evaluation 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-73206-6_7

Download citation

Publish with us

Policies and ethics

Navigation