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Content Provider | IEEE Xplore Digital Library |
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Author | Yen, G.Y. Kuo-Chung Lin |
Copyright Year | 1999 |
Description | Author affiliation: Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA (Yen, G.Y.) |
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 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 their expansion coefficients because the information is diluted across the whole basis. The wavelet packet transform is introduced as an alternative means of extracting time-frequency information from vibration signature. Moreover, with the aid of statistical based feature selection criteria, a lot of feature components containing little discriminant information could be discarded resulting in a feature subset with reduced number of parameters. This significantly reduces the long training time that is often associated with neural network classifier and increases the generalization ability of the neural network classifier. |
Starting Page | 1573 |
Ending Page | 1578 |
File Size | 408947 |
Page Count | 6 |
File Format | |
ISBN | 078035446X |
DOI | 10.1109/CCA.1999.801206 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1999-08-22 |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Wavelet packets Feature extraction Signal analysis Neural networks Condition monitoring Time domain analysis Frequency domain analysis Signal processing Wavelet transforms Data mining |
Content Type | Text |
Resource Type | Article |
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