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An enhanced aircraft engine gas path diagnostic method based on upper and lower singleton type-2 fuzzy logic system

  • Pedro H. S. CalderanoEmail author
  • Mateus G. C. Ribeiro
  • Renan P. F. Amaral
  • Marley M. B. R. Vellasco
  • Ricardo Tanscheit
  • Eduardo P. de Aguiar
Technical Paper
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Abstract

The gas turbine is the most common engine used in the majority of commercial aircraft. Regarding the economic and social importance of aviation, methods that can identify faults in gas turbines with precision are relevant. Aiming to detect and classify the gas turbine faults, this work uses the upper and lower singleton type-2 fuzzy logic system trained by steepest descent method. Succeeding the model presentation, comparisons are performed with other models proposed in the literature to diagnose gas turbine faults. The investigated data set was obtained from the software Propulsion Diagnostic Method Evaluation Strategy, which was developed by the National Aeronautics and Space Administration for benchmarking purposes in aviation gas turbines. The experimental results show that the upper and lower singleton type-2 fuzzy logic system model had a greater detection and classification performance than the models reported in the literature.

Keywords

Aeronautical gas turbine Fuzzy logic system Fault Classification Detection 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Boyce MP (2002) Gas turbine engineering handbook. Gulf Professional Publishing, HoustonGoogle Scholar
  2. 2.
    Morris H (2017) How many planes are there in the world right now? The telegraph. http://www.telegraph.co.uk/travel/travel-truths/how-many-planes-are-there-in-the-world/. Accessed 17 Mar 2018
  3. 3.
    Elsayed EA (2012) Reliability engineering. Wiley, New YorkzbMATHGoogle Scholar
  4. 4.
    Carter TJ (2005) Common failures in gas turbine blades. Eng Fail Anal 12(2):237–247MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hershey JE, Ackerman JF, Hanagandi VKM, Aragones AV, Osborn BE, Chbat NW, Korkosz RA (2002) Method and apparatus for determining an effective jet engine maintenance schedule. US Patent 6,473,677Google Scholar
  6. 6.
    Lee YK, Mavris DN, Volovoi VV, Yuan M, Fisher T (2010) A fault diagnosis method for industrial gas turbines using Bayesian data analysis. J Eng Gas Turbines Power 132(4):041602CrossRefGoogle Scholar
  7. 7.
    Urban LA (1973) Gas path analysis applied to turbine engine condition monitoring. J Aircr 10(7):400–406CrossRefGoogle Scholar
  8. 8.
    Volponi AJ (2013) Gas turbine engine health management: past, present and future trends. In: ASME turbo expo 2013: turbine technical conference and exposition, vol 8, p V008T46A001Google Scholar
  9. 9.
    Marinai L, Probert D, Singh R (2004) Prospects for aero gas-turbine diagnostics: a review. Appl Energy 79(1):109–126CrossRefGoogle Scholar
  10. 10.
    Brotherton T, Jahns G, Jacobs J, Wroblewski D (2000) Prognosis of faults in gas turbine engines. In: Aerospace conference proceedings, vol 6. IEEE, pp 163–171Google Scholar
  11. 11.
    Roemer MJ, Kacprzynski GJ (2000) Advanced diagnostics and prognostics for gas turbine engine risk assessment. In: Aerospace conference proceedings, vol 6. IEEE, pp 345–353Google Scholar
  12. 12.
    Li Y, Nilkitsaranont P (2007) A gas path diagnostic and prognostic approach for gas turbine applications. In: ASME turbo expo 2007: power for land, sea, and air, vol 1, pp 573–584Google Scholar
  13. 13.
    Li Y, Nilkitsaranont P (2009) Gas turbine performance prognostic for condition-based maintenance. Appl Energy 86(10):2152–2161CrossRefGoogle Scholar
  14. 14.
    Kobayashi T, Simon DL (2003) Application of a bank of Kalman filters for aircraft engine fault diagnostics. In: ASME turbo expo 2003, collocated with the 2003 international joint power generation conference, vol 1, pp 461–470Google Scholar
  15. 15.
    Aretakis N, Mathioudakis K, Stamatis A (2004) Identification of sensor faults on turbofan engines using pattern recognition techniques. Control Eng Pract 12(7):827–836CrossRefGoogle Scholar
  16. 16.
    Borguet S, Léonard O (2010) A sparse estimation approach to fault isolation. J Eng Gas Turbines Power 132(2):021601CrossRefGoogle Scholar
  17. 17.
    Hess A, Frith P, Suarez E (2006) Challenges, issues, and lessons learned implementing prognostics for propulsion systems. In: ASME turbo expo 2006: power for land, sea, and air, vol 2, pp 927–935Google Scholar
  18. 18.
    Simon DL, Borguet S, Léonard O, Zhang XF (2013) Aircraft engine gas path diagnostic methods: public benchmarking results. In: ASME turbo expo 2013: turbine technical conference and exposition, vol 136, p V004T06A014Google Scholar
  19. 19.
    Mendel JM (2001) Uncertain rule-based fuzzy logic systems: introduction and new directions. Prentice Hall PTR, Upper Saddle RiverzbMATHGoogle Scholar
  20. 20.
    Rahme S, Meskin N (2015) Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine. Control Eng Pract 38:57–74CrossRefGoogle Scholar
  21. 21.
    Pinelli M, Spina P (2000) Gas turbine field performance determination: sources of uncertainties. In: ASME turbo expo 2000: power for land, sea, and air, vol 3, p V003T03A012Google Scholar
  22. 22.
    Brun K, Kurz R (1998) Measurement uncertainties encountered during gas turbine driven compressor field testing. In: ASME 1998 International gas turbine and aeroengine congress and exhibition, vol 3, p V003T07A001Google Scholar
  23. 23.
    Ganguli R (2001) Application of fuzzy logic for fault isolation of jet engines. In: ASME turbo expo 2001: power for land, sea, and air, vol 4, p V004T04A006Google Scholar
  24. 24.
    Barbosa R, Ferreira S (2012) Industrial gas turbine diagnostics using fuzzy logic. In: ASME turbo expo 2012: turbine technical conference and exposition, vol 1, pp 803–808Google Scholar
  25. 25.
    Teixeira T, Tanscheit R, Ribeiro M (2016) Sistema de inferência fuzzy para diagnóstico de desempenho de turbinas a gás aeronáuticas. In: Proceedings of fourth Brazilian conference on fuzzy systemsGoogle Scholar
  26. 26.
    Simon D (2009) Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) user‘s guide. NASA Glenn Research Center, ClevelandGoogle Scholar
  27. 27.
    de Aguiar EP, Amaral RP, Vellasco MM, Ribeiro MV (2018) An enhanced singleton type-2 fuzzy logic system for fault classification in a railroad switch machine. Electr Power Syst Res 158:195–206CrossRefGoogle Scholar
  28. 28.
    de Aguiar EP, Amaral RP, Vellasco MM, Ribeiro MV (2017) Computing derivatives in interval type-2 fuzzy logic systems trained by steepest descent method for fault classification in a switch machine. In: IEEE international conference on fuzzy systemsGoogle Scholar
  29. 29.
    Jaw LC (2005) Recent advancements in aircraft engine health management (EHM) technologies and recommendations for the next step. In: ASME turbo expo 2005: power for land, sea, and air, vol 1, pp 683–695Google Scholar
  30. 30.
    Simon DL, Bird J, Davison C, Volponi A, Iverson RE (2008) Benchmarking gas path diagnostic methods: a public approach. In: ASME turbo expo 2008: power for land, sea, and air, American society of mechanical engineers, vol 2, pp 325–336Google Scholar
  31. 31.
    Volponi AJ (1999) Gas turbine parameter corrections. J Eng Gas Turbines Power 121(4):613–621CrossRefGoogle Scholar
  32. 32.
    DePold HR, Gass FD (1998) The application of expert systems and neural networks to gas turbine prognostics and diagnostics. In: ASME 1998 international gas turbine and aeroengine congress and exhibition, vol 121, p V005T15A009Google Scholar
  33. 33.
    Anand R, Mehrotra K, Mohan CK, Ranka S (1995) Efficient classification for multiclass problems using modular neural networks. IEEE Trans Neural Netw 6(1):117–124CrossRefGoogle Scholar
  34. 34.
    Mendel JM (2004) Computing derivatives in interval type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 12(1):84–98MathSciNetCrossRefGoogle Scholar
  35. 35.
    Liang Q, Mendel JM (2000) Interval type-2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 8(5):535–550CrossRefGoogle Scholar
  36. 36.
    Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427MathSciNetCrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  • Pedro H. S. Calderano
    • 1
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  • Mateus G. C. Ribeiro
    • 1
  • Renan P. F. Amaral
    • 2
  • Marley M. B. R. Vellasco
    • 3
  • Ricardo Tanscheit
    • 3
  • Eduardo P. de Aguiar
    • 1
  1. 1.Industrial and Mechanical Engineering DepartmentFederal University of Juiz de ForaJuiz de ForaBrazil
  2. 2.Mechanical Engineering DepartmentPontifical Catholic University of Rio de JaneiroRio de JaneiroBrazil
  3. 3.Department of Electrical EngineeringPontifical Catholic University of Rio de JaneiroRio de JaneiroBrazil

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