A hybrid signal pre-processing approach in processing ultrasonic signals with noise

Palanisamy, S., Nagarajah, C.R., Graves, K. and Iovenitti, P. (2009) A hybrid signal pre-processing approach in processing ultrasonic signals with noise. International Journal of Advanced Manufacturing Technology, 42 7-8: 766-771.


Author Palanisamy, S.
Nagarajah, C.R.
Graves, K.
Iovenitti, P.
Title A hybrid signal pre-processing approach in processing ultrasonic signals with noise
Journal name International Journal of Advanced Manufacturing Technology   Check publisher's open access policy
ISSN 0268-3768
1433-3015
0178-0026
Publication date 2009-06
Sub-type Article (original research)
DOI 10.1007/s00170-008-1640-0
Volume 42
Issue 7-8
Start page 766
End page 771
Total pages 6
Place of publication London, U.K
Publisher Springer-Verlag
Language eng
Subject 0910 Manufacturing Engineering
Formatted abstract Ultrasonic techniques have the potential to be used to detect sub-surface defects in aluminium castings. However, ultrasonic sensing techniques have not been successfully used to detect sub-surface defects in aluminium die castings with rough surfaces or in the 'as-cast' state due to the poor quality of signals. Ultrasonic signal noise caused by rough surfaces and grain size variations of the castings is difficult to eliminate. Hence, there is a need to process noisy ultrasonic signals to identify defects within the rough surface castings. This paper documents an investigation of ultrasonic signal analysis using artificial neural networks and hybrid signal pre-processing approaches for the purpose of detecting defects from noisy ultrasonic signals. In this investigation, ultrasonic signals were obtained from aluminium castings with different levels of surface roughness. The signals were first pre-processed using hybrid signal analysis techniques and then classified using an artificial neural network classifier. The hybrid pre-processing techniques utilised various combinations of fast Fourier transform (FFT), wavelet transform (WT) and principal component analysis. The best signal classification performance was generally achieved with a hybrid WT/FFT signal pre-processing technique.
© 2008 Springer-Verlag London Limited.
Keyword Ultrasonic inspection
Castings
Neural networks
Signal processing
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Mechanical & Mining Engineering Publications
 
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