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Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Author

Listed:
  • Yunpeng Cao

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Xinran Lv

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Guodong Han

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Junqi Luan

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

  • Shuying Li

    (College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China)

Abstract

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

Suggested Citation

  • Yunpeng Cao & Xinran Lv & Guodong Han & Junqi Luan & Shuying Li, 2019. "Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network," Energies, MDPI, vol. 12(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4701-:d:296220
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    References listed on IDEAS

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    1. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    2. Valero, Antonio & Correas, Luis & Zaleta, Alejandro & Lazzaretto, Andrea & Verda, Vittorio & Reini, Mauro & Rangel, Victor, 2004. "On the thermoeconomic approach to the diagnosis of energy system malfunctions," Energy, Elsevier, vol. 29(12), pages 1875-1887.
    3. 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.
    4. Qingcai Yang & Shuying Li & Yunpeng Cao & Fengshou Gu & Ann Smith, 2018. "A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach," Energies, MDPI, vol. 11(12), pages 1-21, November.
    5. Valero, Antonio & Correas, Luis & Zaleta, Alejandro & Lazzaretto, Andrea & Verda, Vittorio & Reini, Mauro & Rangel, Victor, 2004. "On the thermoeconomic approach to the diagnosis of energy system malfunctions," Energy, Elsevier, vol. 29(12), pages 1889-1907.
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