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Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation

Author

Listed:
  • Jinfu Liu

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Mingliang Bai

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Zhenhua Long

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Jiao Liu

    (AVIC Shenyang Aircraft Design & Research Institute, Shenyang 110000, China)

  • Yujia Ma

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Daren Yu

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Failures of the gas turbine hot components often cause catastrophic consequences. Early fault detection can detect the sign of fault occurrence at an early stage, improve availability and prevent serious incidents of the plant. Monitoring the variation of exhaust gas temperature (EGT) is an effective early fault detection method. Thus, a new gas turbine hot components early fault detection method is developed in this paper. By introducing a priori knowledge and quantum particle swarm optimization (QPSO), the exhaust gas temperature profile continuous distribution model is established with finite EGT measuring data. The method eliminates influences of operating and ambient condition changes and especially the gas swirl effect. The experiment reveals the presented method has higher fault detection sensitivity.

Suggested Citation

  • Jinfu Liu & Mingliang Bai & Zhenhua Long & Jiao Liu & Yujia Ma & Daren Yu, 2020. "Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation," Energies, MDPI, vol. 13(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5950-:d:445153
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    Cited by:

    1. Paweł Ocłoń & Maciej Ławryńczuk & Marek Czamara, 2021. "A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation," Energies, MDPI, vol. 14(16), pages 1-15, August.
    2. Long, Zhenhua & Bai, Mingliang & Ren, Minghao & Liu, Jinfu & Yu, Daren, 2023. "Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network," Energy, Elsevier, vol. 272(C).
    3. Ruijun Guo & Guobin Zhang & Qian Zhang & Lei Zhou & Haicun Yu & Meng Lei & You Lv, 2021. "An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique," Energies, MDPI, vol. 14(16), pages 1-18, August.

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