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A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

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  • Feng Lu

    (Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada)

  • Chunyu Jiang

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

  • Jinquan Huang

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

  • Yafan Wang

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

  • Chengxin You

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

Abstract

Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS) is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM) is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL) by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.

Suggested Citation

  • Feng Lu & Chunyu Jiang & Jinquan Huang & Yafan Wang & Chengxin You, 2016. "A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis," Energies, MDPI, vol. 9(10), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:828-:d:80617
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    References listed on IDEAS

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    1. 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.
    2. 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:

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    2. Jie Liu & Qiu Tang & Wei Qiu & Jun Ma & Junfeng Duan, 2021. "Probability-Based Failure Evaluation for Power Measuring Equipment," Energies, MDPI, vol. 14(12), pages 1-16, June.
    3. Valentina Zaccaria & Moksadur Rahman & Ioanna Aslanidou & Konstantinos Kyprianidis, 2019. "A Review of Information Fusion Methods for Gas Turbine Diagnostics," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    4. Hui Wang & Jingxuan Sun & Jianbo Sun & Jilong Wang, 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    5. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
    6. Jiao Liu & Jinfu Liu & Daren Yu & Myeongsu Kang & Weizhong Yan & Zhongqi Wang & Michael G. Pecht, 2018. "Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network," Energies, MDPI, vol. 11(8), pages 1-18, August.
    7. Liu, Jie & Xu, Huoyao & Peng, Xiangyu & Wang, Junlang & He, Chaoming, 2023. "Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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