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Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE (2008)
Dec. 19, 2008 to Dec. 20, 2008
ISBN: 978-0-7695-3490-9
pp: 864-868
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.

W. Hongxing, Y. Lin, X. Zengpu and Z. Congling, "Method for Extraction Wavelet Packets' Coefficients in Loudspeaker Fault Detection Based on PCA," 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application. PACIIA 2008(PACIIA), Wuhan, 2008, pp. 864-868.
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