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Research on aero-engine vibration fault based on neural network and information fusion technology

  • China Aviation Industry Corporation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

It is an important means for determining the conditions and making fault analysis of the aero-engine by measuring its vibration. Because the different features give different analysis results for vibration fault, in order to integration these information, the results of the different Back Propagation (BP) neural networks were fused by applying the Dempster-Shafer (D-S) evidential theory of the information fusion and the basic belief assignment function was established according to the statistical parameters of the networks. The analysis results from the aero-engine vibration signals show that the information fusion method can improve the reliability of the diagnosis and decrease the uncertainty.

Original languageEnglish
Title of host publication4th Conference on Vibration Measurement, Together with 23rd TC3 Conference on the Measurement of Force, Mass and Torque and 13th TC5 Conference on the Measurement of Hardness
PublisherEuropean Association of Geoscientists and Engineers, EAGE
ISBN (Electronic)9781510844933
StatePublished - 2017
Externally publishedYes
Event4th Conference on Vibration Measurement, Together with 23rd TC3 Conference on the Measurement of Force, Mass and Torque and 13th TC5 Conference on the Measurement of Hardness - Helsinki, Finland
Duration: 30 May 20171 Jun 2017

Publication series

Name4th Conference on Vibration Measurement, Together with 23rd TC3 Conference on the Measurement of Force, Mass and Torque and 13th TC5 Conference on the Measurement of Hardness

Conference

Conference4th Conference on Vibration Measurement, Together with 23rd TC3 Conference on the Measurement of Force, Mass and Torque and 13th TC5 Conference on the Measurement of Hardness
Country/TerritoryFinland
CityHelsinki
Period30/05/171/06/17

Keywords

  • Aero-engine
  • BP neural network
  • D-S evidence theory
  • Vibration

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