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SDA-RVM Based Approach for Surge Fault Detection and Diagnosis During Aero-Engine Take-Off Process

  • Ji-Bang Li
  • Shuo ZhangEmail author
  • Xiao-Yu Sun
  • Wei-Guo Xia
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 480)

Abstract

Under various operation conditions, the take-off process of aero-engine is regarded as a typical positive system. Meaning while, the aero-engine surge caused by exerting force in the take-off process brings catastrophic risk to the flight safety and affects overall aero-engine performance. Therefore the precise forecasting of aero-engine rotating stall development process under complex conditions is an effective method for the detection and diagnosis of aero-engine surge fault. In order to avoid the roughness result of the binary classification and the difficulty of feature extraction within high dimensional data for traditional machine learning (ML) approaches, SDA-RVM is developed to provide an accurate rotating stall detection and a surge warning window. Firstly, the SDA is implemented to extract the implicit feature beneath the high dimensional data. Then, the RVM is carried out to calculate the stall trigger probability under the reconstructed vector input. Finally, the surge alert window is identified according to the stall probability. The result of various ML algorithm is compared with the data of on service aero-engine, demonstrating the efficacy of the proposed SDA-RVM approach.

Keywords

Surge fault diagnosis Rotating stall detection Relevance vector machine (RVM) Stacked de-nosing auto-encoders (SDA) 

Notes

Acknowledgements

We are grateful for the financial support of the Fundamental Research Funds for the Central Universities in China (No. DUT16RC(3)115) and State Key Laboratory of Robotics Fund Project (No. 2017-O03).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ji-Bang Li
    • 1
  • Shuo Zhang
    • 1
    Email author
  • Xiao-Yu Sun
    • 1
  • Wei-Guo Xia
    • 1
  1. 1.Dalian University of TechnologyDalianChina

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