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Engine-fault diagnostics:an optimisation procedure

Author

Listed:
  • Sampath, Suresh
  • Ogaji, Stephen
  • Singh, Riti
  • Probert, Douglas

Abstract

A diagnostic process capable of providing an early warning of a fault in a gas turbine is of tremendous value to the user and can result in substantial financial savings. The approach in the Genetic Algorithm based technique adopted is to treat the problem of engine diagnostics as an optimisation exercise using sensor-based and mathematical behavioural model based information. The engine performance model would simulate a range of possible combinations of potential faults (i.e the effects of model-based information) and a comparison would be made with values of the actual (sensor-based) parameters obtained from an engine. The difference between the actual and simulated values of would be converted into a suitable objective-function and the aim of the optimisation technique such as the genetic algorithm would be to minimise the objective function. The technique has given promising results for simple cycle engines.

Suggested Citation

  • Sampath, Suresh & Ogaji, Stephen & Singh, Riti & Probert, Douglas, 2002. "Engine-fault diagnostics:an optimisation procedure," Applied Energy, Elsevier, vol. 73(1), pages 47-70, September.
    • Handle: RePEc:eee:appene:v:73:y:2002:i:1:p:47-70
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    Citations

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    Cited by:

    1. Maria Grazia De Giorgi & Nicola Menga & Antonio Ficarella, 2023. "Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies," Energies, MDPI, vol. 16(6), pages 1-37, March.
    2. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    3. Haiqin Qin & Jie Zhao & Likun Ren & Bianjiang Li & Zhengguang Li, 2022. "Performance Degradation Evaluation of Low Bypass Ratio Turbofan Engine Based on Flight Data," Sustainability, MDPI, vol. 14(13), pages 1-12, July.
    4. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    5. Koziel, Sylvie & Hilber, Patrik & Westerlund, Per & Shayesteh, Ebrahim, 2021. "Investments in data quality: Evaluating impacts of faulty data on asset management in power systems," Applied Energy, Elsevier, vol. 281(C).

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