Skip to main content
SpringerLink
Log in

Analysis of the Impact of Degradation on Gas Turbine Performance Using the Support Vector Machine (SVM) Method

  • Conference paper
  • First Online:
Proceedings of International Conference of Aerospace and Mechanical Engineering 2019

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

  • 859 Accesses

Abstract

Degradation is an important aspect in the operation and maintenance of gas turbines since it affects maintenance costs substantially. Hence, the study of degradation in terms of recoverable and non-recoverable degradation is crucial to formulate a correct maintenance strategy and, as a result, achieve optimum maintenance cost. In this paper, the impact of recoverable and non-recoverable degradation towards compressor discharge pressure, fuel gas flow, and exhaust gas temperature are measured during the start of run period that reflects the time period from the new gas turbine condition to the first scheduled offline crank wash, which normally approximates to 8000 running hours. For the study, a three-unit single speed light industrial gas turbine that drives an electrical generator to power up an offshore platform located in a tropical climate is considered. The measurement of the parameters has been conducted using the support vector machine (SVM) method.

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
Softcover Book
USD 219.99
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. Soares C (2015) In: Gas turbines: a handbook of air, land and sea applications, 2nd Edn. Elsevier

    Google Scholar 

  2. Sabri HAR, Rahim ARA, Majid H (2013) Concepts of turbomachinery selection for upstream oil and gas facilities project. In: 2nd International conference on mechanical, automotive and aerospace engineering, Kuala Lumpur

    Google Scholar 

  3. Wan A, Chen J, Zheng L, Hall P, Ji Y, Gu X (2018) Prognostic of gas turbine: a condition-based maintenance approach based on multi-environmental time similarity. Mech Syst Signal Process 109:150–165

    Google Scholar 

  4. Botros KK, Hartloper C, Golshan H, Rogers D (2016) Recoverable versus unrecoverable degradations of gas turbines employed in five natural gas compressor stations. J Eng Gas Turbine Power 138(2)

    Google Scholar 

  5. Igie U, Pilidis P, Fouflias D, Ramsden K, Laskaridis P (2014) Industrial gas turbine performance: compressor fouling and on-line washing. J Turbomach 136(10):101001

    Google Scholar 

  6. Cruz Manzo S, Panov V, Zhang Y (2018) Gas path fault and degradation modelling in twin-shaft gas turbines. Machines 6(4):43

    Google Scholar 

  7. Tahan M, Tsoutsanis E, Muhammad M, Abdul Karim ZA (2017) Performance-based health monitoring, diagnostic and prognostics for condition-based maintenance of gas turbine: a review. Appl Energy 198:122–144

    Google Scholar 

  8. Petek J, Hamilton P (2005) Performance monitoring for gas turbines. Orbit 25

    Google Scholar 

  9. Boyce MP (2012) Gas turbine engineering handbook, 4th edn. Elsevier

    Google Scholar 

  10. Kurz R, Meher-Homji C, Brun K (2014) Gas turbine degradation. In: Turbomachinery and pump symposia (Pump & Turbo 2014). Houston

    Google Scholar 

  11. Hepperle N, Therkorn D, Schneider E, Staudacher S (2011) Assessment of gas turbine and combined cycle power plant performance degradation. In: ASME turbo expo. Vancouver

    Google Scholar 

  12. Diakunchak SI (1992) Performance deterioration in industrial gas turbines. J Eng Gas Turbines Power 114(2):161–168

    Google Scholar 

  13. Brun K, Kurz R, Simmons HR (2006) Aerodynamic instability and life-limiting effects of inlet and interstage water injection into gas turbines. ASME J Gas Turbine Power 128(3):617–625

    Google Scholar 

  14. Zwebek AI, Pilidis P (2004) Degradation effects on combined cycle power plant performance- Part iii: gas and steam turbine component degradation effects. ASME J Gas Turbine and Power 126(2):306–315

    Google Scholar 

  15. Kurz R, Brun K (2007) Gas turbine tutorial—maintenance and operating practices effect on degradation life. In: Proceedings of the thirty-six turbomachinery symposium. Turbomachinery Laboratory, Houston

    Google Scholar 

  16. Amare DF, Aklilu TB, Gilani SI (2016) Effects of performance deterioration on gas path measurement in and industrial turbines. ARPN J Eng Appl Sci 11(24):14202–14207

    Google Scholar 

  17. Frith PC (1994) The effect of compressor rotor tip crops on turboshaft engine performance. ASME J Gas Turbine Power 2

    Google Scholar 

  18. Graf M, Wong T, Greitzer E, Marble F, Tan C, Shin HW (1997) Effects on non-asymmetric tip clearance on axial compressor performance. In: ASME international gas turbine and aero-engine congress and exposition

    Google Scholar 

  19. Melcher KJ, Kypuros J (2003) Towards a fast response active turbine tip clearance control. In: Symposium on air breathing engines. Ohio

    Google Scholar 

  20. Yin Z, Hou J (2015) Recent advances on svm based on faults diagnosis and process monitoring in complicated and industrial processes. Neurocomputing 174(Part B):643–650

    Google Scholar 

  21. Hamed A, Tabakoff W, Singh D (1998) Modelling of compressor performance deterioration due to erosion. Int J Rotating Mach 4(4):243–248

    Google Scholar 

  22. Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnostic. Mech Signal Proc J 21(6):2560–2574

    Google Scholar 

  23. Wu CH, Ho JM, Lee DT (2003) Travel-time prediction with support vector regression. IEEE Trans Intell Transp Syst 2:1438–1442

    Google Scholar 

  24. Clarke SM, Griebsch JH, Simpson TW (2005) Analysis of support vector regression for approximation of complex engineering analyses. J Mech Des 127(6):1077–1087

    Google Scholar 

  25. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1996) Support vector regression machines. Neural Information Processing Systems (NIPS), pp 155–161

    Google Scholar 

  26. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Networks 10(5):988–999

    Google Scholar 

  27. Kurz R, Brun K (2000) Gas turbine degradation—what makes the map? In: Turbomachinery symposium, 29th Edn. Houston

    Google Scholar 

  28. Allen CW, Holcomb CM, Olievera MD (2017) Gas turbine machinery diagnostic: a brief review and sample application. In: Turbomachinery conference and exposition, GT2017. ASME, North Carolina

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Khairul Fata B. Ahmad Asnawi & Tamiru Alemu Lemma

Authors
  1. Khairul Fata B. Ahmad Asnawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tamiru Alemu Lemma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khairul Fata B. Ahmad Asnawi .

Editor information

Editors and Affiliations

  1. School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia

    Parvathy Rajendran

  2. School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia

    Nurul Musfirah Mazlan

  3. School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia

    Aslina Anjang Ab Rahman

  4. School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia

    Nurulasikin Mohd Suhadis

  5. School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia

    Norizham Abdul Razak

  6. School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia

    Mohd Shukur Zainol Abidin

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Asnawi, K.F.B.A., Lemma, T.A. (2020). Analysis of the Impact of Degradation on Gas Turbine Performance Using the Support Vector Machine (SVM) Method. In: Rajendran, P., Mazlan, N., Rahman, A., Suhadis, N., Razak, N., Abidin, M. (eds) Proceedings of International Conference of Aerospace and Mechanical Engineering 2019 . Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-4756-0_38

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4756-0_38

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4755-3

  • Online ISBN: 978-981-15-4756-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation