DocumentCode :
2286678
Title :
Study on Bearing Load Identification of Rotor Bearing System Based on Artificial Neural Networks
Author :
Liang Qunlong ; Yang Zhaojian ; Sun Huer ; Pang Xinyu
Author_Institution :
Coll. of Mech. Eng., Taiyuan Univ. of Technol., Taiyuan, China
Volume :
3
fYear :
2010
fDate :
13-14 March 2010
Firstpage :
568
Lastpage :
571
Abstract :
The identification of bearing load is essentially a mapping process from vibration response to bearing load. The characteristics of neural networks is that using sample data without establishing a mathematical model of system may achieve nonlinear mapping of system from Rn (n is the node number of input)space to Rm (m is the node number of output )space. This paper presents a identification method of bearing load of rotor system based on neural networks technique. It has many characteristics such as simple model, controllable accuracy, fast computation, and it has memory capability and the ability to further study. It may provide a theoretical basis for online monitoring the bearing load.
Keywords :
identification; machine bearings; neural nets; power engineering computing; rotors; vibrations; artificial neural networks; bearing load identification; mapping process; memory capability; nonlinear mapping; online monitoring; rotor bearing system; vibration response; Artificial neural networks; Automation; Frequency domain analysis; Frequency measurement; Mechanical variables measurement; Mechatronics; Neural networks; Parameter estimation; Sun; Vibration measurement; Bearing load; Identification; Neural networks; Rotor system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
Type :
conf
DOI :
10.1109/ICMTMA.2010.450
Filename :
5459101
Link To Document :
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