Real-Time Fault Diagnosis for Gas Turbine Blade Based on Output-Hidden Feedback Elman Neural Network

  • Pengcheng Zhuo (卓鹏程)
  • Ying Zhu (朱颖)
  • Wenxuan Wu (邬雯喧)
  • Junqing Shu (舒俊清)
  • Tangbin Xia (夏唐斌)Email author


In order to remotely monitor and maintain large-scale complex equipment in real time, China Telecom plans to create a total solution that integrates remote data collection, transmission, storage, analysis and prediction. This solution can provide manufacturers with proactive, systematic, integrated operation and maintenance service, and the data analysis and health forecasting are the most important part. This paper conducts health management for the turbine blades. Elman neural network, and improved Elman neural network, i.e., outputhidden feedback (OHF) Elman neural network are studied as the main research methods. The results verify the applicability of OHF Elman neural network.

Key words

gas turbine blade health management output-hidden feedback (OHF) Elman neural network 

CLC number

TP 183 V 23 

Document code


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  1. [1]
    WANG J T, LIU H W, XU D. A research on the large-scale complex equipment spare parts demand prediction based on the BP neural network model and Markov chain [C]//Proceedings of 2013 International Conference on Industrial Engineering and Management Science. [s.l.]: Springer, 2013: 774–779.Google Scholar
  2. [2]
    CAI B P, LIU H L, XIE M. A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks [J]. Mechanical Systems and Signal Processing, 2016, 80: 31–44.CrossRefGoogle Scholar
  3. [3]
    ESLAMI M, SHAYESTEH M, POURAHMADI M R. Optimal design of PID-based low-pass filter for gas turbine using intelligent method [J]. IEEE Access, 2018, 6: 15335–15345.CrossRefGoogle Scholar
  4. [4]
    WAN A P, GU F, CHEN J H, et al. Prognostics of gas turbine: A condition-based maintenance approach based on multi-environmental time similarity [J]. Mechanical Systems and Signal Processing, 2018, 109: 150–165.CrossRefGoogle Scholar
  5. [5]
    JIA X D, JIN C, BUZZA M, et al. Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves [J]. Renewable Energy, 2016, 99: 1191–1201.CrossRefGoogle Scholar
  6. [6]
    FORBES G L, RANDALL R B. Estimation of turbine blade natural frequencies from casing pressure and vibration measurements [J]. Mechanical Systems and Signal Processing, 2013, 36(2): 549–561.CrossRefGoogle Scholar
  7. [7]
    PENNACCHI P, CHATTERTON S, BACHSCHMID N, et al. A model to study the reduction of turbine blade vibration using the snubbing mechanism [J]. Mechanical Systems and Signal Processing, 2011, 25: 1260–1275.CrossRefGoogle Scholar
  8. [8]
    LIU C, JIANG D X. Crack modeling of rotating blades with cracked hexahedral finite element method [J]. Mechanical Systems and Signal Processing, 2014, 46: 426–423.Google Scholar
  9. [9]
    COMPARE M, BELLANI L, ZIO E. Reliability model of a component equipped with PHM capabilities [J]. Reliability Engineering and System Safety, 2017, 168: 4–11.CrossRefGoogle Scholar
  10. [10]
    JARDINE A K S, LIN D M, BANJEVIC D. A review on machinery diagnostics and prognostics implementing condition-based maintenance [J]. Mechanical Systems and Signal Processing, 2006, 20: 1483–1510.CrossRefGoogle Scholar
  11. [11]
    XIA T B, DONG Y F, XIAO L, et al. Recent advances in prognostics and health management for advanced manufacturing paradigms [J]. Reliability Engineering and System Safety, 2018, 178: 255–268.CrossRefGoogle Scholar
  12. [12]
    ZHONG J H, WONG P K, YANG Z X. Fault diagnosis of rotating machinery based on multiple probabilistic classifiers [J]. Mechanical Systems and Signal Processing, 2018, 108: 99–114.CrossRefGoogle Scholar
  13. [13]
    LEE J, WU F J, ZHAO W Y, et al. Prognostics and health management design for rotary machinery systems: Reviews, methodology and applications [J]. Mechanical Systems and Signal Processing, 2014, 42: 314–334.CrossRefGoogle Scholar
  14. [14]
    HANACHI H, MECHEFSKE C, LIU J, et al. Performance-based gas turbine health monitoring, diagnostics, and prognostics: A survey [J]. IEEE Transactions on Reliability, 2018, 67(3): 1340–1363.CrossRefGoogle Scholar
  15. [15]
    HU Q P, XIE M, NG S H, et al. Robust recurrent neural network modeling for software fault detection and correction prediction [J]. Reliability Engineering and System Safety, 2007, 92: 332–340.CrossRefGoogle Scholar
  16. [16]
    OH K Y, PARK J Y, LEE J S, et al. A novel method and its field tests for monitoring and diagnosing blade health for wind turbines [J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(6): 1726–1733.Google Scholar
  17. [17]
    LI Y F, LI G X. YAN J. Fault diagnosis of wind turbine blades based on fuzzy theory [C]//International Conference on Control, Automation and Systems Engineering. Singapore: IEEE, 2011: 1–3.Google Scholar
  18. [18]
    GAO J R, WANG Y Q. The research on the methods of diagnosing the steam turbine based on the Elman neural network [J]. Journal of Software Engineering and Applications, 2013, 6: 87–90.CrossRefGoogle Scholar
  19. [19]
    SHI X H, LIANG Y C, LEE H P, et al. Improved Elman networks and applications for controlling ultrasonic motors [J]. Applied Artificial Intelligence, 2004, 18: 603–629.CrossRefGoogle Scholar
  20. [20]
    WANG Y N, ZHANG F J, CUI T, et al. Fault diagnosis for manifold absolute pressure sensor (MAP) of diesel engine based on Elman neural network observer [J]. Chinese Journal of Mechanical Engineering, 2016, 29(2): 386–395.CrossRefGoogle Scholar
  21. [21]
    DRAKE P R, MILLER K A. Improved self-feedback gain in the context layer of a modified Elman neural network [J]. Mathematical and Computer Modelling of Dynamical Systems, 2002, 8(3): 307–311.CrossRefzbMATHGoogle Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Pengcheng Zhuo (卓鹏程)
    • 1
  • Ying Zhu (朱颖)
    • 1
  • Wenxuan Wu (邬雯喧)
    • 1
  • Junqing Shu (舒俊清)
    • 1
  • Tangbin Xia (夏唐斌)
    • 1
    Email author
  1. 1.State Key Laboratory of Mechanical System and Vibration, Department of Industrial Engineering, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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