Advertisement

Economic Efficiency of Data-Driven Fault Diagnosis and Prognosis Techniques in Maintenance and Repair Organizations

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 117)

Abstract

Maintenance costs represent a significant part of economic expenses for the aviation industry. The development of data-driven fault diagnosis, prognosis, and health management techniques create unique opportunities to increase the efficiency of Maintenance and Repair Organization (MRO) activity and, as a result, to reduce airline maintenance costs. Nevertheless, despite the widespread data-driven prognosis ideas, the economic success of MRO in data-driven prognosis remains rather frugal and local. It is yet to demonstrate its positive strategic and long-term impact on corporate economics comparable to the one of the LEAN philosophy. Many MRO still do not know how to approach predictive maintenance techniques properly – due to lack of a consistent theory and strategy for the development of data-driven projects, and unavailability of a standard with proven mechanisms and methodology that would allow a MRO to make decisions on development of predictive projects and evaluate their economic performance. This paper reviews the existing literature related to the economic efficiency of data-driven projects, defines current gaps in economic analysis and proposes the methodology of how to support an MRO in dealing with predictive maintenance projects effectively on macro, semi-macro and micro levels.

Keywords

Predictive maintenance Airplane health management Economic analysis Gaps Macroanalysis Microanalysis 

References

  1. 1.
    Ayeni, P.: Enhancing competitive advantage through successful Lean realisation within the Aviation Maintenance Repair and Overhaul (MRO) industry (2015)Google Scholar
  2. 2.
    Levitt, J.: Complete Guide to Preventive and Predictive Maintenance. Industrial Press, New York (2003)Google Scholar
  3. 3.
    Wang, H., Ye, X., Yin, M.: Study on predictive maintenance strategy. Int. J. Sci. Technol. 9, 295–300 (2016)Google Scholar
  4. 4.
    Nascimento, R.G., Viana, F.A.C.: Fleet prognosis with physics-informed recurrent neural networks. CoRR. abs/1901.05512 (2019)Google Scholar
  5. 5.
    Wang, Q., Zheng, S., Farahat, A.K., Serita, S., Gupta, C.: Remaining useful life estimation using functional data analysis. CoRR. abs/1904.06442 (2019)Google Scholar
  6. 6.
    Verhagen, W.J.C., De Boer, L.W.M.: Predictive maintenance for aircraft components using proportional hazard models. J. Ind. Inf. Integr. 12, 23–30 (2018).  https://doi.org/10.1016/j.jii.2018.04.004CrossRefGoogle Scholar
  7. 7.
    Isermann, R.: Fault-Diagnosis Applications. Springer, Berlin, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-12767-0
  8. 8.
    Ding, S.X.: Data-Driven Design of Fault Diagnosis and Fault-Tolerant Control Systems. Springer, London (2014).  https://doi.org/10.1007/978-1-4471-6410-4
  9. 9.
    Shen, Q., Jiang, B., Shi, P.: Fault Diagnosis and Fault-Tolerant Control Based on Adaptive Control Approach. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-52530-3
  10. 10.
    Si, X.-S., Zhang, Z.-X., Hu, C.-H.: Data-Driven Remaining Useful Life Prognosis Techniques. Springer, Berlin, Heidelberg (2017).  https://doi.org/10.1007/978-3-662-54030-5
  11. 11.
    Esperon-Miguez, M., John, P., Jennions, I.K.: A review of integrated vehicle health management tools for legacy platforms: challenges and opportunities. Prog. Aerosp. Sci. 56, 19–34 (2013).  https://doi.org/10.1016/j.paerosci.2012.04.003CrossRefGoogle Scholar
  12. 12.
    Zerhouni, N.: From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics. ISTE Ltd., Hoboken, NJ (2016)Google Scholar
  13. 13.
    Niu, G.: Data-Driven Technology for Engineering Systems Health Management. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-2032-2
  14. 14.
    Spiegel, S., Mueller, F., Weismann, D., Bird, J.: Cost-sensitive learning for predictive maintenance. CoRR. abs/1809.10979 (2018)Google Scholar
  15. 15.
    He, Y., Han, X., Gu, C., Chen, Z.: Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems. Adv. Mech. Eng. 10, 168781401775146 (2018).  https://doi.org/10.1177/1687814017751467CrossRefGoogle Scholar
  16. 16.
    Balaban, E., Bansal, P., Stoelting, P., Saxena, A., Goebel, K.F., Curran, S.: A diagnostic approach for electro-mechanical actuators in aerospace systems. In: 2009 IEEE Aerospace Conference, pp. 1–13. IEEE, Big Sky, MT, USA (2009).  https://doi.org/10.1109/AERO.2009.4839661
  17. 17.
    Guo, H., Xiao, G., Mrad, N., Yao, J.: Fiber optic sensors for structural health monitoring of air platforms. Sensors 11, 3687–3705 (2011).  https://doi.org/10.3390/s110403687CrossRefGoogle Scholar
  18. 18.
    Azam, M., Pattipati, K., Allanach, J., Poll, S., Patterson-Hine, A.: In-flight fault detection and isolation in aircraft flight control systems. In: 2005 IEEE Aerospace Conference, pp. 3555–3565. IEEE, Big Sky, MT, USA (2005).  https://doi.org/10.1109/AERO.2005.1559659
  19. 19.
    Long, H., Wang, X.: Aircraft fuel system diagnostic fault detection through expert system. In: 2008 7th World Congress on Intelligent Control and Automation, pp. 7104–7107. IEEE, Chongqing (2008).  https://doi.org/10.1109/WCICA.2008.4594020
  20. 20.
    Ntantis, E.L., Botsaris, P.N.: Diagnostic methods for an aircraft engine performance. J. Eng. Sci. Technol. Rev. 8, 64–72 (2015).  https://doi.org/10.25103/jestr.084.10
  21. 21.
    Zhang, X., Tang, L., Decastro, J.: Robust fault diagnosis of aircraft engines: a nonlinear adaptive estimation-based approach. IEEE Trans. Control Syst. Technol. 21, 861–868 (2013).  https://doi.org/10.1109/TCST.2012.2187057CrossRefGoogle Scholar
  22. 22.
    Markowitz, H.: Portfolio selection. J. Finance 7, 77–91 (1952).  https://doi.org/10.1111/j.1540-6261.1952.tb01525.xCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.S7 Engineering LLCMoscowRussia
  2. 2.Transport and Telecommunication InstituteRigaLatvia

Personalised recommendations