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A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach

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  • Qingcai Yang

    (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)

  • Yunpeng Cao

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

  • Fengshou Gu

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Ann Smith

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

To ensure reliable and efficient operation of gas turbines, multiple model (MM) approaches have been extensively studied for online fault detection and isolation (FDI). However, current MM-FDI approaches are difficult to directly apply to gas path FDI, which is one of the common faults in gas turbines and is understood to mainly be due to the high complexity and computation in updating hypothetical gas path faults for online applications. In this paper, a fault contribution matrix (FCM) based MM-FDI approach is proposed to implement gas path FDI over a wide operating range. As the FCM is realized via an additive term of the healthy model set, the hypothetical models for various gas path faults can be easily established and updated online. In addition, a gap metric analysis method for operating points selection is also proposed, which yields the healthy model set from the equal intervals linearized models to approximate the nonlinearity of the gas turbine over a wide range of operating conditions with specified accuracy and computational efficiency. Simulation case studies conducted on a two-shaft marine gas turbine demonstrated the proposed approach is capable of adaptively updating hypothetical model sets to accurately differentiate both single and multiple faults of various gas path faults.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3316-:d:186026
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    References listed on IDEAS

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    1. Ogaji, Stephen & Sampath, Suresh & Singh, Riti & Probert, Douglas, 2002. "Novel approach for improving power-plant availability using advanced engine diagnostics," Applied Energy, Elsevier, vol. 72(1), pages 389-407, May.
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    3. Feng Lu & Jinquan Huang & Yiqiu Lv, 2013. "Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach," Energies, MDPI, vol. 6(1), pages 1-22, January.
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    Cited by:

    1. 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.
    2. Yunpeng Cao & Junqi Luan & Guodong Han & Xinran Lv & Shuying Li, 2019. "A Marine Gas Turbine Fault Diagnosis Method Based on Endogenous Irreversible Loss," Energies, MDPI, vol. 12(24), pages 1-18, December.
    3. Binbin Yan & Minghui Hu & Kun Feng & Zhinong Jiang, 2021. "Enhanced Component Analytical Solution for Performance Adaptation and Diagnostics of Gas Turbines," Energies, MDPI, vol. 14(14), pages 1-20, July.

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