Earticle

초록

영어

The performance of the aero-engine is an important safeguard to flying security. We can diagnose and predict the fault type through obtaining and analyzing the vibration signals based on the fault characters of the aero-engine. But, because of the complexity of the aero-engine’s structure, the vibration signals acquired from multiple groups of the piezoelectric sensors are often composed of several signal aliasing and other noise jamming, etc. Thus, the vibration signals are in the nonlinear or weak nonlinear state, the traditional blind source separation (BSS) algorithms usually adopt linear hypothesis to approximate equivalent to the nonlinear problems, which leading to the separation results not ideal even wrong. This paper applied a kind of multilayer perceptron BP neural network algorithm to realize the aero-engine vibration signal separation with high precision through the simulation and experiment, which proved this algorithm has a certain practical value to the aero-engine fault diagnosis and prediction.

목차

Abstract
 1. Introduction
 2. Aero-Engine Fault Feature
  2.1. Rotor Imbalance
  2.2. Rotor Axis Center Line Deviation
  2.3. Rotor rubbing fault
  2.4. Blade Vibration Fault
  2.5. Bearing Vibration Fault
  2.6. Gear Drive Fault
 3. Multilayer Perceptron BP Neural Network algorithm
  3.1. Nonlinear BSS Model
  3.2 BP Neural Network
 4. Algorithm Simulation
 5. Aero-Engine Fault Diagnosis Experiment
 6. Conclusion
 References

키워드

저자

• Wen Xinling [ Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China ]
• Chen Yu [ Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China ]
• Liu Zhaoyu [ Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou 450015, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605