Skip to main content
SpringerLink
Log in

Towards an Intelligent Decision Support System for Aircraft Troubleshooting

  • Conference paper
  • First Online:
Proceedings of 10th International Conference on Recent Advances in Civil Aviation

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Currently, troubleshooting an aircraft remains a promising area for automation and intellectualization. At the same time, existing solutions in this area in the form of electronic technical manuals do not always meet the requirements of technical personnel when searching and troubleshooting aircraft. In this regard, the development of another class of systems based on artificial intelligence methods is relevant. These systems can provide not only the search and elimination of failures and malfunctions but also self-learning by accumulating the experience. This paper proposes the basic principles of such an intelligent system, called the AirTech Assistant, designed for use by technical personnel engaged in the maintenance and repair of the power supply system of the Sukhoi Superjet (RRJ-95) aircraft. In particular, a fragment of the conceptual model of the domain is given, functional, operational, and quality requirements are formulated, architecture, as well as fundamental algorithms and a stack of implementation technologies, are defined. The main components of the designed system will be expert systems implementing case-based and rule-based reasoning. An additional study was conducted in terms of testing the formalism of event trees for knowledge base engineering.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

  1. Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59. https://doi.org/10.3233/AIC-1994-7104

    Article  Google Scholar 

  2. Jackson P (1998) Introduction to expert systems. Addison-Wesley

    MATH  Google Scholar 

  3. AirNav-Maintenance. https://www.airnav.com/maintenance/index.html. Accessed 23 Oct 2021

  4. Savvina AM (2019) Predlozhenie po modernizacii bortovoj sistemy tekhnicheskogo obsluzhivaniya samoleta SSJ 100 (Proposal for the modernization of the onboard maintenance system of the SSJ 100 aircraft). Crede Experto Transp Soc Educ Lang 3(22):27–35 (in Russian)

    Google Scholar 

  5. Perfiliev OV, Ryzhakov S, Dolzhikov VA (2018) Intellektual’naya sistema poiska neispravnosti na samolete (Intelligent system for finding mal-functions an aircraft). Proc Samara Sci Center Russian Acad Sci 4(3):326–331 (in Russian)

    Google Scholar 

  6. Bergmann R (2002) Experience management: foundations, development methodology, and internet-based applications. In: Lecture notes in artificial intelligence, p 2432.https://doi.org/10.1007/3-540-45759-3

  7. Watson I (1999) Case-based reasoning is a methodology not a technology. Knowl-Based Syst 12:303–308. https://doi.org/10.1016/S0950-7051(99)00020-9

    Article  Google Scholar 

  8. De Mantaras LR, Mcsherry D, Bridge D, Leake D, Smyth B, Craw S, Faltings B, Maher ML, Cox MT, Forbus K, Keane M, Aamodt A, Watson I (2005) Retrieval, reuse, revision and retention in case-based reasoning. Knowl Eng Rev 20(3):215–240. https://doi.org/10.1017/S0269888906000646

    Article  Google Scholar 

  9. Nikolaychuk OA, Yurin AY (2006) Automating the identification of mechanical systems’ technical state using case-based reasoning. In: IEEE intelligent systems. Processing of the 3rd international IEEE conference intelligent systems, pp 30–35. https://doi.org/10.1109/IS.2006.348389

  10. Yurin AY (2015) Group decision-making methods for adapting solutions derived from case-based reasoning. Sci Tech Inf Process 42(5):375–381. https://doi.org/10.3103/S014768821505010X

    Article  Google Scholar 

  11. Chastikov AP, Gavrilova TA, Belov DL (2003) Razrabotka ekspertnyh sistem. Sreda CLIPS (Development of expert systems. CLIPS). BHV-Petersburg, 608 p (in Russian)

    Google Scholar 

  12. Giarratano J, Riley G (2004) Expert systems: principles and programming. https://doi.org/10.5860/choice.27-4542

  13. Forgy C (1982) Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif Intell 19:17–37. https://doi.org/10.1016/B978-0-934613-53-8.50041-8

    Article  Google Scholar 

  14. Lieberman H, Paternò F, Klann M, Wulf V (2006) End-user development: an emerging paradigm. Human-Comput Interaction Ser 9:1–7. https://doi.org/10.1007/1-4020-5386-X_1

    Article  Google Scholar 

  15. Barricelli BR, Cassano F, Fogli D, Piccinno A (2019) End-user development, end-user programming and end-user software engineering: a systematic mapping study. J Syst Softw 149:101–137. https://doi.org/10.1016/J.JSS.2018.11.041

    Article  Google Scholar 

  16. Yurin AY (2020) Technology for prototyping expert systems based on transformations (PESoT): a method. CEUR Workshop Proc 2677:36–50

    Google Scholar 

  17. GOST 20911-89 (2009) Tekhnicheskaya diagnostika. Terminy i opredeleniya (Technical diagnostics. Terms and definitions). Terms and definitions. Standartinform. Moscow (in Russian)

    Google Scholar 

  18. Berman AF, Nikolaychuk OA, Yurin AY, Pavlov AI (2014) A methodology for the investigation of the reliability and safety of unique technical systems. Proc Inst Mech Eng Part O J Risk Reliab 228:29–38. https://doi.org/10.1177/1748006X13494820

    Article  Google Scholar 

  19. Zagorulko YA, Borovikova OI (2018) Podhod k realizacii patternov soderzhaniya pri razrabotke ontologij nauchnyh predmetnyh oblastej. Sistemnaya informatika (Approach to the implementation of content pat-terns for the development of ontology of scientific subject domains). Syst Inform 12:27–40. https://doi.org/10.31144/si.2307-6410.2018.n12 (in Russian)

  20. Maltugueva GS, Yurin AY (2019) Improving case-based reasoning with the aid of multi-criteria and group decision-making methods. In: Proceedings of the 42nd international convention on information and communication technology, electronics and microelectronics (MIPRO), pp 1031–1036. https://doi.org/10.23919/MIPRO.2019.8756874

  21. Zhuravlev IY, Gurevitch IB (1989) Raspoznavanie, klassifikaciya, prognoz. Matematicheskie metody i ih primenenie (Pattern recognition, classification, forecasting: mathematical techniques and their application) 2:5–72 (in Russian)

    Google Scholar 

  22. Dorodnykh NO, Kotov YV, Nikolaychuk OA, Popov VM, Yurin AY (2021) End-user development of knowledge bases for semi-automated formation of task cards. CEUR Workshop Proc 2913:60–73. https://doi.org/10.47350/ICCS-DE.2021.05

    Article  Google Scholar 

  23. Yurin AY, Dorodnykh NO (2020) Personal knowledge base designer: software for expert systems prototyping. SoftwareX 11:100411. https://doi.org/10.1016/j.softx.2020.100411

    Article  Google Scholar 

Download references

Acknowledgements

The present study was supported by the Ministry of Education and Science of the Russian Federation (Project no. 121030500071-2 “Methods and technologies of a cloud-based service-oriented platform for collecting, storing and processing large volumes of multi-format interdisciplinary data and knowledge based upon the use of artificial intelligence, model-driven approach and machine learning”).

Author information

Authors and Affiliations

  1. Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of Russian Academy of Sciences (ISDCT SB RAS), Irkutsk, Russia

    Aleksandr Yurin

  2. Irkutsk Branch of Moscow State Technical University of Civil Aviation, Irkutsk, Russia

    Aleksandr Yurin, Yuri Kotlov, Vladimir Popov & Sergey Mishin

Authors
  1. Aleksandr Yurin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuri Kotlov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vladimir Popov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sergey Mishin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Aleksandr Yurin , Yuri Kotlov , Vladimir Popov or Sergey Mishin .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Moscow State Technical University of Civil Aviation, Irkutsk Branch, Irkutsk, Russia

    Oleg Anatolyevich Gorbachev

  2. Department of System Engineering, Northwestern Polytechnical University, Xi’an, China

    Xiaoguang Gao

  3. Department of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Bo Li

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yurin, A., Kotlov, Y., Popov, V., Mishin, S. (2023). Towards an Intelligent Decision Support System for Aircraft Troubleshooting. In: Gorbachev, O.A., Gao, X., Li, B. (eds) Proceedings of 10th International Conference on Recent Advances in Civil Aviation. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-3788-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-3788-0_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-3787-3

  • Online ISBN: 978-981-19-3788-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation