An application of genetic neural networks in fault diagnosis of aero-engine vibration

Author

Fengling Zhang ; Zhi Wang

Author_Institution

Coll. of Aerosp. Eng., Shenyang Aerosp. Univ., Shenyang, China

fYear

2013

fDate

23-25 July 2013

Firstpage

116

Lastpage

121

Abstract

This paper presents a hybrid method named genetic neural networks(GNN) which combines genetic algorithm(GA) with neural networks(NN). Fault diagnostic results of aero-engine vibration based on GNN are obtained by setting typical vibration fault modes, including rotor imbalance, rotor disalignment and looseness of rotors. It is shown that this method is superior to the traditional neural network in improving the accuracy and rapidity of vibration fault diagnosis.

Keywords

aerospace engines; fault diagnosis; genetic algorithms; mechanical engineering computing; neural nets; rotors (mechanical); vibrations; GA; GNN; aero-engine vibration; genetic algorithm; genetic neural networks; hybrid method; rotor disalignment; rotor imbalance; rotor looseness; vibration fault diagnosis accuracy improvement; vibration fault diagnosis rapidity improvement; vibration fault modes; Artificial neural networks; Biological neural networks; Fault diagnosis; Genetic algorithms; Rotors; Vibrations; aero-engine vibration; fault diagnosis; genetic algorithm; genetic neural networks; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2013 Ninth International Conference on

Conference_Location

Shenyang

Type

conf

DOI

10.1109/ICNC.2013.6817955

Filename

6817955