Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/5147
Title: Design of artificial neural networks for rotor dynamics analysis of rotating machine systems
Authors: Taplak, Hamdi
Uzmay, Ibrahim
Yıldırım, Sahin
Keywords: Neural network
Shaft vibration
Rotor dynamic
Artificial neural network
Rotating machine system
Issue Date: Jun-2005
Publisher: CSIR
Series/Report no.: G 06 N 3/02
Abstract: A neural network predictor is designed for analyzing vibration parameters of the rotating system. The vibration parameters (amplitude, velocity, acceleration in vertical direction) are measured at the bearing points. The system’s vibration and noise are analyzed with and without load. The designed neural predictor has three (input, hidden, output) layers. In the hidden layer, 10 neurons are used for approximation. The results show that the network is useful as an analyzer of such systems in experimental applications. The neural networks are validated for reduced test data with unknown faults.
Description: 411-419
URI: http://hdl.handle.net/123456789/5147
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.64(06) [June 2005]

Files in This Item:
File Description SizeFormat 
JSIR 64(6) 411-419.pdf433.47 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.