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An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis

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Listed:
  • Feng Lu

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Jipeng Jiang

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Jinquan Huang

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Xiaojie Qiu

    (Aviation Motor Control System Institute, Aviation Industry Corporation of China, Wuxi 214063, China)

Abstract

To improve gas-path performance fault pattern recognition for aircraft engines, a new data-driven diagnostic method based on hidden Markov model (HMM) is proposed. A redundant sensor somewhat interferes with fault diagnostic results of the HMM, and it also increases the computational burden. The contribution of this paper is to develop an iterative reduced kernel principal component analysis (IRKPCA) algorithm to extract fault features from original high-dimension observation without large additional calculation load and combine it with the HMM for engine gas-path fault diagnosis. The optimal kernel features are obtained by iterative sequential forward selection of the IRKPCA, and the features with lower dimensions are contracted through a trade-off between the fault information and modeling data scale in reduced kernel space. The similarity degree is designed to simplify the HMM modeling data using fault kernel features. Test results show that the proposed methodology brings a significant improvement in diagnostic confidence and computational efforts in the applications of a turbofan engine fault diagnosis during its steady and dynamic process.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1807-:d:157265
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    References listed on IDEAS

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    1. 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.
    2. Akram Khaleghei & Viliam Makis, 2015. "Model parameter estimation and residual life prediction for a partially observable failing system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(3), pages 190-205, April.
    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. Feng Lu & Yafan Wang & Jinquan Huang & Yihuan Huang, 2015. "Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter," Energies, MDPI, vol. 8(12), pages 1-17, December.
    5. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    6. 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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    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. 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).

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