Title :
Fault diagnosis for rotor system based on AR-PCA and BP neural network
Author :
Wang, Zhen ; Sun, Lan ; Qi, Guibing
Author_Institution :
Dept. of Mech. Eng., Dalian Univ., Dalian, China
Abstract :
This paper introduces a method for the fault diagnosis of a rotor system. For a vibration signal of a rotor system fault, an AR model is established first, and then the related parameter and amplitude spectrum of this mode can be obtained, etc. The experiments show the above-mentioned method can effectively diagnose the fault of a rotor system.
Keywords :
autoregressive processes; backpropagation; fault diagnosis; mechanical engineering computing; neural nets; principal component analysis; rotors; vibrations; AR-PCA; BP neural network; amplitude spectrum; fault diagnosis; rotor system; rotor system fault; vibration signal; Artificial neural networks; Fault diagnosis; Mathematical model; Principal component analysis; Rotors; Testing; Training; AR model; BP neural network; PCA; rotor system;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on
Conference_Location :
XianNing
Print_ISBN :
978-1-61284-458-9
DOI :
10.1109/CECNET.2011.5768717
