Brunel University Research Archive (BURA) >
Schools >
School of Engineering and Design >
School of Engineering and Design Research papers >

Please use this identifier to cite or link to this item:

Title: Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery
Authors: Kaewkongka, T
Rakowski, RT
Jones, BE
Keywords: Wavelet transform
neural network
rotodynamic machinery
Publication Date: 2001
Publisher: IEEE
Citation: Proc of IEEE Conference IMTC, Budapest. 3 1962- 1966.
Abstract: This paper describes a novel method of rotodynamic machine condition monitoring using a wavelet transform and a neural network. A continuous wavelet transform is applied to the signals collected from accelerometer. The transformed images are then extracted as unique characteristic features relating to the various types of machine conditions. In the experiment, four types of machine operating conditions have been investigated: a balanced shaft; an unbalanced shaft, a misaligned shaft and a defective bearing. The back propagation neural network (BPNN) is used as a tool to evaluate the performance of the proposed method. The experimental results result in a recognition rate of 90 percent.
ISBN: 0-7803-6646-8
Appears in Collections:School of Engineering and Design Research papers
Advanced Manufacturing and Enterprise Engineering (AMEE)

Files in This Item:

File Description SizeFormat
Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery.pdf448.97 kBAdobe PDFView/Open

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


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan