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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang Hongxing Xu Zengpu Zhou Congling Yang Lin |
| Copyright Year | 2008 |
| Description | Author affiliation: Sch. of Mech. Eng., Tianjin Univ. of Sci. & Technol., Tianjin (Wang Hongxing; Xu Zengpu; Zhou Congling; Yang Lin) |
| Abstract | This paper presents a new method using principal component analysis (PCA) to eliminate data redundancy in loudspeaker fault detection. It uses wavelet packet transformation (WPT) to decompose the loudspeaker acoustics signal into 32 packet node signals. Then, get the mean, max, standard deviation and correlation coefficient of every node envelopment .With the way of observing, it gets 63 coefficients from 128 ones which are helpful for detection of fault. Using the new way above, 32 coefficients are removed from the 63. The failed loudspeaker can be found with the help of artificial neural network (ANN). It is proved that the method is very effective in experiment. |
| Starting Page | 864 |
| Ending Page | 868 |
| File Size | 567312 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769534909 |
| DOI | 10.1109/PACIIA.2008.375 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Loudspeakers Fault diagnosis Fault detection Signal processing Time frequency analysis Wavelet packets Hardware Data mining Principal component analysis Acoustic waves |
| Content Type | Text |
| Resource Type | Article |
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