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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
Article
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Abstract

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 

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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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