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A coupling diagnosis method of sensors faults in gas turbine control system

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  • Sun, Rongzhuo
  • Shi, Licheng
  • Yang, Xilian
  • Wang, Yuzhang
  • Zhao, Qunfei

Abstract

Gas turbines usually operate under complex conditions, such as frequent start-stop, complex environment (dust, salt fog). There are many sensors equipped in a gas turbine for the sake of monitoring and control. The sensors may fail to output normal signals since working continuously for a long time and in the harsh conditions. To avoid misjudgment of gas turbine control system due to sensors’ failures, it’s necessary to diagnose the sensors faults from the output signals beforehand. In this paper, a coupling diagnosis method of sensors faults in gas turbine control system based on machine learning was proposed. We coupled the wavelet energy entropy (WEE) and support vector regression (SVR) for sensor fault diagnosis where WEE was used to extract the signals features and SVR was used to classify the types of faults. A sensors faults database with five typical types was built by using the experimental data of a 7000 kW gas turbine under different operating conditions to verify the accuracy and effectiveness of the proposed coupling method. The results show that the accuracy of the coupling method is more than 90% with a shorter diagnosis time.

Suggested Citation

  • Sun, Rongzhuo & Shi, Licheng & Yang, Xilian & Wang, Yuzhang & Zhao, Qunfei, 2020. "A coupling diagnosis method of sensors faults in gas turbine control system," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220311063
    DOI: 10.1016/j.energy.2020.117999
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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. Dong, Ming & He, David, 2007. "Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis," European Journal of Operational Research, Elsevier, vol. 178(3), pages 858-878, May.
    3. Salahshoor, Karim & Kordestani, Mojtaba & Khoshro, Majid S., 2010. "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers," Energy, Elsevier, vol. 35(12), pages 5472-5482.
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    Citations

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    Cited by:

    1. Linhai Zhu & Jinfu Liu & Yujia Ma & Weixing Zhou & Daren Yu, 2020. "A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines," Energies, MDPI, vol. 13(18), pages 1-19, September.
    2. Yang, Xilian & Zhao, Qunfei & Wang, Yuzhang & Cheng, Kanru, 2023. "Fault signal reconstruction for multi-sensors in gas turbine control systems based on prior knowledge from time series representation," Energy, Elsevier, vol. 262(PA).
    3. Huang, Yufeng & Tao, Jun & Sun, Gang & Wu, Tengyun & Yu, Liling & Zhao, Xinbin, 2023. "A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis," Energy, Elsevier, vol. 270(C).
    4. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    5. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    6. Martí de Castro-Cros & Manel Velasco & Cecilio Angulo, 2021. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review," Energies, MDPI, vol. 14(24), pages 1-27, December.

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