Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Model-Based Faults Diagnostics of Single Shaft Gas Turbine Using Fuzzy Faults Tolerant Control

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

This paper proposes a new fault-tolerant control approach for gas turbines, based on fuzzy techniques that allow real-time observation of their behavior. The approach consists of detecting and locating faults, and then determining the appropriate control action to keep the turbine in stable operation. This improves the efficiency and lifespan of the turbine, and reduces maintenance costs with optimal planning. The state space model of the turbine is subjected to a diagnostic procedure based on Type-1 and Type-2 fuzzy models, using the operational data of the different operating points studied. The Luenberger observer is used in the fault detection mechanism, to characterize turbine component malfunctions by comparing the observed real behaviors and the fuzzy models. The results show that the fault-tolerant fuzzy control has ensured the stability and availability of the turbine during fault occurrence, with good performance in detection, isolation and reconfiguration.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.
Fig. 15.
Fig. 16.
Fig. 17.
Fig. 18.

REFERENCES

  1. Feng, K., Xiao, Yu., Li, Z., Jiang, Z., and Gu, F., Gas turbine blade fracturing fault diagnosis based on broadband casing vibration, Measurement, 2023, vol. 214, p. 112718. https://doi.org/10.1016/j.measurement.2023.112718

    Article  Google Scholar 

  2. Aissat, S., Hafaifa, A., Iratni, A., Hadroug, N., and Chen, X., Fuzzy decoupled-states multi-model identification of gas turbine operating variables through the use of their operating data, ISA Trans., 2023, vol. 133, pp. 384–396. https://doi.org/10.1016/j.isatra.2022.07.005

    Article  Google Scholar 

  3. Molla Salilew, W., Ambri Abdul Karim, Z., and Alemu Lemma, T., Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine, Alexandria Eng. J., 2022, vol. 61, no. 12, pp. 12635–12651. https://doi.org/10.1016/j.aej.2022.06.026

    Article  Google Scholar 

  4. Alaoui, M., Alshammari, O.S., Iratni, A., Hafaifa, A., and Jerbi, H., Gas turbine speed monitoring using a generalized predictive adaptive control algorithm, Stud. Inf. Control, 2022, vol. 31, no. 3, pp. 87–96. https://doi.org/10.24846/v31i3y202208

    Article  Google Scholar 

  5. Ben Rahmoune, M., Iratni, A., Hafaifa, A., and Colak, I., Gas turbine vibration detection and identification based on dynamic artificial neural networks, Electrotehnica, Electron.a, Autom., 2023, vol. 71, no. 2, pp. 19–27. https://doi.org/10.46904/eea.23.71.2.1108003

    Article  Google Scholar 

  6. Djeddi, C., Hafaifa, A., Iratni, A., Hadroug, N., and Chen, X., Robust diagnosis with high protection to gas turbine failures identification based on a fuzzy neuro inference monitoring approach, J. Manuf. Syst., 2021, vol. 59, pp. 190–213. https://doi.org/10.1016/j.jmsy.2021.02.012

    Article  Google Scholar 

  7. Hadroug, N., Hafaifa, A., Alili, B., Iratni, A., and Chen, X., Fuzzy diagnostic strategy implementation for gas turbine vibrations faults detection: Towards a characterization of symptom–fault correlations, J. Vib. Eng. Technol., 2022, vol. 10, no. 1, pp. 225–251. https://doi.org/10.1007/s42417-021-00373-z

    Article  Google Scholar 

  8. Djeddi, A.Z., Hafaifa, A., Hadroug, N., and Iratni, A., Gas turbine availability improvement based on long short-term memory networks using deep learning of their failures data analysis, Process Saf. Environ. Prot. J., 2022, vol. 159, pp. 1–25. https://doi.org/10.1016/j.psep.2021.12.050

    Article  Google Scholar 

  9. Amare, F.D., Gilani, S.I., Aklilu, B.T., and Mojahid, A., Two-shaft stationary gas turbine engine gas path diagnostics using fuzzy logic, J. Mech. Sci. Technol., 2017, vol. 31, no. 11, pp. 5593–5602. https://doi.org/10.1007/s12206-017-1053-9

    Article  Google Scholar 

  10. Mohammadi, E. and Montazeri-Gh, M., A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration, Aerosp. Sci. Technol., 2015, vol. 46, pp. 82–93. https://doi.org/10.1016/j.ast.2015.07.002

    Article  Google Scholar 

  11. Salahshoor, K., Khoshro, M.S., and Kordestani, M., Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems, Simul. Modell. Pract. Theory, 2011, vol. 19, no. 5, pp. 1280–1293. https://doi.org/10.1016/j.simpat.2011.01.005

    Article  Google Scholar 

  12. Benrahmoune, M., Ahmed, H., Mouloud, G., and Xiaoqi, C., Detection and modeling vibrational behavior of a gas turbine based on dynamic neural networks approach, Strojnícky Casopis – J. Mech. Eng., 2018, vol. 68, no. 3, pp. 143–166. https://doi.org/10.2478/scjme-2018-0032

    Article  Google Scholar 

  13. Simani, S., Alvisi, S., and Venturini, M., Data-driven design of a fault tolerant fuzzy controller for a simulated hydroelectric system, IFAC-PapersOnLine, 2015, vol. 48, no. 21, pp. 1090–1095. https://doi.org/10.1016/j.ifacol.2015.09.672

    Article  Google Scholar 

  14. Raikar, C. and Ganguli, R., Denoising signals used in gas turbine diagnostics with ant colony optimized weighted recursive median filters, INAE Lett., 2017, vol. 2, no. 3, pp. 133–143. https://doi.org/10.1007/s41403-017-0023-y

    Article  Google Scholar 

  15. Abbasi Nozari, H., Aliyari Shoorehdeli, M., Simani, S., and Dehghan Banadaki, H., Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques, Neurocomputing, 2012, vol. 91, pp. 29–47. https://doi.org/10.1016/j.neucom.2012.02.014

    Article  Google Scholar 

  16. Berrios, R., Núñez, F., and Cipriano, A., Fault tolerant measurement system based on Takagi–Sugeno fuzzy models for a gas turbine in a combined cycle power plant, Fuzzy Sets Syst., 2011, vol. 174, no. 1, pp. 114–130. https://doi.org/10.1016/j.fss.2011.02.011

    Article  MathSciNet  Google Scholar 

  17. Wu, X. and Liu, Yi., Leakage detection for hydraulic IGV system in gas turbine compressor with recursive ridge regression estimation, J. Mech. Sci. Technol., 2017, vol. 31, no. 10, pp. 4551–4556. https://doi.org/10.1007/s12206-017-0901-y

    Article  Google Scholar 

  18. Salahshoor, K. and Kordestani, M., Design of an active fault tolerant control system for a simulated industrial steam turbine, Appl. Math. Modell., 2014, vol. 38, nos. 5–6, pp. 1753–1774. https://doi.org/10.1016/j.apm.2013.09.015

    Article  Google Scholar 

  19. Lu, A.-Ya. and Yang, G.-H., Secure Luenberger-like observers for cyber–physical systems under sparse actuator and sensor attacks, Automatica, 2018, vol. 98, pp. 124–129. https://doi.org/10.1016/j.automatica.2018.09.003

    Article  MathSciNet  Google Scholar 

  20. Yang, B., Liu, M., Kim, H., and Cui, X., Luenberger-sliding mode observer based fuzzy double loop integral sliding mode controller for electronic throttle valve, J. Process Control, 2018, vol. 61, pp. 36–46. https://doi.org/10.1016/j.jprocont.2017.11.004

    Article  Google Scholar 

  21. Hu, Yu., Lam, J., and Liang, J., Consensus of multi-agent systems with Luenberger observers, J. Franklin Inst., 2013, vol. 350, no. 9, pp. 2769–2790. https://doi.org/10.1016/j.jfranklin.2013.06.006

    Article  MathSciNet  Google Scholar 

  22. Guo, S., Jiang, B., Zhu, F., and Wang, Z., Luenberger-like interval observer design for discrete-time descriptor linear system, Syst. Control Lett., 2019, vol. 126, pp. 21–27. https://doi.org/10.1016/j.sysconle.2019.02.005

    Article  MathSciNet  Google Scholar 

  23. Guo, S., Jiang, B., Zhu, F., and Wang, Z., Luenberger-like interval observer design for discrete-time descriptor linear system, Syst. Control Lett., 2019, vol. 126, pp. 21–27. https://doi.org/10.1016/j.sysconle.2019.02.005

    Article  MathSciNet  Google Scholar 

  24. Ortega, R., Praly, L., Aranovskiy, S., Yi, B., and Zhang, W., On dynamic regressor extension and mixing parameter estimators: Two Luenberger observers interpretations, Automatica, 2018, vol. 95, pp. 548–551. https://doi.org/10.1016/j.automatica.2018.06.011

    Article  MathSciNet  Google Scholar 

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa, DZ 17000, Djelfa, DZ, Algeria

    Hakim Bagua, Belgacem Said Khaldi, Abdelhamid Iratni & Ahmed Hafaifa

  2. Joint Research Team on Gas Turbines, Faculty of Science and Technology, University of Djelfa, 17000, Djelfa, DZ, Algeria

    Hakim Bagua & Belgacem Said Khaldi

  3. Department of Electrical Engineering, Faculty of Science and Technology, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, DZ 34030, El Anasser, Bordj Bou Arreridj, Algeria

    Abdelhamid Iratni & Ahmed Hafaifa

  4. Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, İstanbul Nisantasi University, 34398, Sarıyer, İstanbul, Turkey

    Ilhami Colak

Authors
  1. Hakim Bagua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Belgacem Said Khaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelhamid Iratni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Hafaifa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ilhami Colak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Hafaifa.

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Allerton Press remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hakim Bagua, Khaldi, B.S., Iratni, A. et al. Model-Based Faults Diagnostics of Single Shaft Gas Turbine Using Fuzzy Faults Tolerant Control. Aut. Control Comp. Sci. 58, 117–130 (2024). https://doi.org/10.3103/S0146411624700020

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411624700020

Keywords: