Skip to main content
SpringerLink
Log in

Analysis of performance deterioration of a micro gas turbine and the use of neural network for predicting deteriorated component characteristics

  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

Deteriorated performance data of a micro gas turbine were generated and the artificial neural network was applied to predict the deteriorated component characteristics. A program to simulate operation of a micro gas turbine was set up and deterioration of each component (compressor, turbine and recuperator) was modeled by changes in the component characteristic parameters such as compressor and turbine efficiency, their flow capacities and recuperator effectiveness and pressure drop. Single and double faults (degradation of single and two parameters) were simulated. The neural network was trained with a majority of the generated deterioration data. Then, the remaining data were used to check the predictability of the neural network. Given measurable performance parameters as inputs to the neural network, characteristic parameters of each component were predicted and compared with original data. The neural network produced sufficiently accurate prediction. Using a smaller number of input parameters decreased prediction accuracy. However, an acceptable accuracy was observed even without information on several input parameters.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. I. S. Diakunchak, Performance deterioration in industrial gas turbines, Journal of Engineering for Gas Turbines and Power, 114 (1992) 161–168.

    Article  Google Scholar 

  2. Y. G. Li, Performance analysis based gas turbine diagnostics: A review, Proc. of The IMechE, Part A - J. of Power and Energy, 216 (2002) 363–377.

    Article  Google Scholar 

  3. C. R. Davison and A. M. Birk, Steady state and transient modeling of a micro-turbine with comparison to operating engine, Proc. of ASME Turbo Expo, Amsterdam, The Netherlands, (2004) ASME paper GT2004-53378.

  4. J. Yin, M. S. Li and W. Huang, Performance analysis and diagnostics of a small gas turbine, Proc. of the Int. Gas Turbine Congress, Tokyo, Japan, (2003) IGTC2003Tokyo TS-006.

  5. J. J. Lee, J. E. Yoon, T. S. Kim, and J. L. Sohn, Performance test and component characteristics evaluation of a micro gas turbine, J. of Mechanical Science and Technology, 21(1) (2007) 141–152.

    Article  Google Scholar 

  6. K. Kanelopoulos, A. Stamatis and K. Mathioudakis, Incorporating neural networks into gas turbine performance diagnostics, Proc. of ASME Turbo Expo, Orlando, USA, (1997) ASME Paper 97-GT-35.

  7. A. J. Volponi, H. R. DePold, R. Ganguli and C. Daguang, The use of Kalman Filter and neural networks methodologies in gas turbine performance diagnostics: A comparative study, Proc. of ASME Turbo Expo, Munich, Germany, (2000) ASME Paper 2000-GT-0547.

  8. C. Romessis, A. Stamatis, and K. Mathioudakis, A parametric investigation of the diagnostic ability of probabilistic neural networks on turbofan engines, Proc. of ASME Turbo Expo, New Orleans, USA, (2001) ASME Paper 2001-GT-0011.

  9. M. Ghoreyshi and R. Singh, Using neural network for diagnostics of an industrial gas turbine, Proc. of ASME Turbo Expo, Amsterdam, The Netherlands, (2004) ASME paper GT2004-53638.

  10. R. Bettocchi, M. Pinelli, P. R. Spina and M. Venturini, Artificial intelligence for the diagnostics of gas turbines. part I: neural network approach, Proc. of ASME Turbo Expo, Reno, USA, (2005) ASME Paper GT2005-68026.

  11. E. E. B. Gomes, D. Mc. Caffrey, M. J. M. Garces, A. L. Polizakis and P. Pilidis, Comparative analysis of microturbines performance deterioration and diagnostics, Proc. of ASME Turbo Expo, Barcelona, Spain, (2006) ASME paper GT2006-90304.

  12. R. M. Gustavo, B. Roberto, B. Cleverson and L. Isaias, Gas turbine fault detection and isolation using MLP artificial neural network, Proc. of ASME Turbo Expo, Montreal, Canada, (2007) ASME paper GT2007-27987.

  13. J. J. Lee, J. E. Yoon and T. S. Kim, Operation simulation of a microturbine based on test data, J. of Fluid Machinery (in Korean), 9(6) (2006) 22–28.

    Article  Google Scholar 

  14. Aspen Technology, AspenOne HYSYS, ver. 2004. 02 (2004).

  15. R. Kurz and K. Brun, Degradation in gas turbine systems, Trans. ASME, Journal of Engineering for Gas Turbines and Power, 123(1) (2001) 70–77.

    Article  Google Scholar 

  16. A. Zwebeck and P. Pilidis, Degradation effects on combined cycle power plant performance — Part 1: gas turbine cycle power component degradation effects, Trans. ASME, Journal of Engineering for Gas Turbines and Power, 125(1) (2003) 651–657.

    Article  Google Scholar 

  17. Alyuda Research Inc, Alyuda Neuro Intelligence ver 2.2 (2006).

Download references

Author information

Authors and Affiliations

  1. Graduate School, Inha University, Incheon, 402-751, Korea

    Jae Eun Yoon & Jong Jun Lee

  2. Dept. of Mechanical Engineering, Inha University, Incheon, 402-751, Korea

    Tong Seop Kim

  3. School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, 151-742, Korea

    Jeong L. Sohn

Authors
  1. Jae Eun Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jong Jun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tong Seop Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeong L. Sohn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tong Seop Kim.

Additional information

This paper was recommended for publication in revised form by Associate Editor Ohchae Kwon

Mr. J. E. Yoon received his MS degree from Dept. of Mechanical Engineering, Inha University in 2008. His thesis topic was test and simulation of micro gas turbines. He has been working at LG Digital Appliance Company.

Mr. J.J. Lee received his MS degree from Dept. of Mechanical Engineering, Inha University in 2006, and is now Doctoral student at the same department. His research topics include simulation and diagnosis of gas turbines.

Prof. T.S. Kim received his PhD degree from Dept. of Mechanical Engineering, Seoul Na-tional University in 1995. He has been with Dept of Mechanical Engineering, Inha Univeristy since 2000, and is Associate Professor as of Oct. 2008. His research area is aero-thermodynamc simulation and test of gas turbine systems including microturbine and their components. His recent research concern also includes analysis on fuel cells and fuel cell/gas turbine hybrid systems.

Prof. J.L. Shon received his PhD degree from Dept. of Mechanical Engineering, The University of Alabama in Huntsville in 1986. He has been with School of Mechanical & Aerospace Engineering, Seoul National University since 2000, and is BK Associate Professor as of Oct. 2008. His research area is design, simulation and test of gas turbine system and components. He is also interested in researches on fuel cells and fuel cell/gas turbine hybrid systems.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yoon, J.E., Lee, J.J., Kim, T.S. et al. Analysis of performance deterioration of a micro gas turbine and the use of neural network for predicting deteriorated component characteristics. J Mech Sci Technol 22, 2516–2525 (2008). https://doi.org/10.1007/s12206-008-0808-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-008-0808-8

Keywords

Search

Navigation