Wavelet packet feature extraction for vibration monitoring
Yen, G.G.
Lin, K.-C.
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Jun 2000
Volume: 47,
Issue: 3
On page(s): 650-667
ISSN: 0278-0046
References Cited: 26
CODEN: ITIED6
INSPEC Accession Number: 6624314
Digital Object Identifier: 10.1109/41.847906
Current Version Published: 2002-08-06
Abstract
Condition monitoring of dynamic systems based on vibration
signatures has generally relied upon Fourier-based analysis as a means
of translating vibration signals in the time domain into the frequency
domain. However, Fourier analysis provided a poor representation of
signals well localized in time. In this case, it is difficult to detect
and identify the signal pattern from the expansion coefficients because
the information is diluted across the whole basis. The wavelet packet
transform (WPT) is introduced as an alternative means of extracting
time-frequency information from vibration signatures. The resulting WPT
coefficients provide one with arbitrary time-frequency resolution of a
signal. With the aid of statistical-based feature selection criteria,
many of the feature components containing little discriminant
information could be discarded, resulting in a feature subset having a
reduced number of parameters without compromising the classification
performance. The extracted reduced dimensional feature vector is then
used as input to a neural network classifier. This significantly reduces
the long training time that is often associated with the neural network
classifier and improves its generalization capability
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