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References (16)
-
N. Vyas, D. Satishkumar (2001)
Artificial neural network design for fault identification in a rotor-bearing systemMechanism and Machine Theory, 36
-
G. Cybenko (1992)
Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals and Systems, 5
-
C. Lee, Young-Don Joh, Young-Dae Kim (1990)
Automatic Modal Balancing of Flexible Rotors During Operation: Computer Controlled Balancing HeadProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 204
-
K. Funahashi
On the approximate realisation of continuous mappings by neural networks -
10.1016/0893-6080(89)90003-8
-
R. Schoen, B. Lin, T. Habetler, J. Schlag, Sam Farag (1994)
An unsupervised, on-line system for induction motor fault detection using stator current monitoringProceedings of 1994 IEEE Industry Applications Society Annual Meeting, 1
-
G. Genta (1992)
Vibration of Structures and Machines: Practical Aspects -
10.1007/978-1-4757-1111-0
-
R. Schoent, T. HabetlerS, F. Kamran, R. Bartheld (1994)
Motor bearing damage detection using stator current monitoringProceedings of 1994 IEEE Industry Applications Society Annual Meeting, 1
-
C. Cempel (1991)
Condition evolution of machinery and its assessment from passive diagnostic experimentMechanical Systems and Signal Processing, 5
-
10.1109/28.475697
-
I. Mayes (1994)
Use of Neutral Networks for on-Line Vibration MonitoringProceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 208
-
10.1007/BF02551274
-
S. Haykin (1998)
Neural Networks: A Comprehensive Foundation -
D. Baillie, J. Mathew (1996)
A COMPARISON OF AUTOREGRESSIVE MODELING TECHNIQUES FOR FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGSMechanical Systems and Signal Processing, 10
-
F. Niordson (1975)
Dynamics of Rotors
- Publisher
- Emerald Publishing
- Copyright
- Copyright © 2006 Emerald Group Publishing Limited. All rights reserved.
- ISSN
- 0036-8792
- DOI
- 10.1108/00368790610640082
- Publisher site
- See Article on Publisher Site
Abstract
Purpose – To improve the application neural networks predictors on bearing systems and to investigate the exact neural model of the ball‐bearing system. Design/methodology/approach – A feed forward neural network is designed to model‐bearing system. Two results are compared for finding the exact model of the system. Findings – The results of the proposed neural network predictor gives superior performance for analysing the behaviour of ball bearing undergoing loading deformation. Research limitations/implications – The results of the proposed neural network exactly follows desired results of the system. Neural network predictor can be employed in practical applications. Practical implications – As theoretical and practical study is evaluated together, it is hoped that ball‐bearing designers and researchers will obtain significant results in this area. Originality/value – This paper fulfils an identified research results need and offers practical investigation for an academic career and research. Also, It should be very helpful for industrial application of ball‐bearing systems.
Journal
Industrial Lubrication and Tribology – Emerald Publishing
Published: Jan 1, 2006
Keywords: Bearings; Neural nets