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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xian Guang-lin Zhang Yu Liu Lie-gen Xian Guang-ming |
| Copyright Year | 2005 |
| Description | Author affiliation: Res. Inst. of Comput. Application, South China Univ. of Technol., Guangzhou, China (Xian Guang-lin; Zhang Yu; Liu Lie-gen) |
| Abstract | Conventional wavelet transform (WT) omits some useful details information of fault signals since it only decomposes low frequency band in a higher scale. In this paper, a novel intelligent system is presented for real-time detection and diagnosis of the fault signals. The model consists of wavelet packet analysis (WPA) unit and support vector machines (SVMs) unit. When signals are decomposed in wavelet packet space, different frequency bands are processed from original signals adequately. WPA can improve abilities of feature extraction than conventional WT. We use a large number of samples to compare the accuracy rate of three kinds kernel function of SVMs, the results indicate that accuracy of Gaussian kernel is higher than polynomial kernel and multilayer perceptron (MLP) kernel. No matter whether the data set is small or huge, accurate classification rate of SVMs is better than RBF and BP neural network methods for normal and exceptional subjects. |
| Starting Page | 591 |
| Ending Page | 594 |
| File Size | 195704 |
| Page Count | 4 |
| File Format | |
| ISBN | 078039335X |
| DOI | 10.1109/WCNM.2005.1544113 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-09-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wavelet transforms Real time systems Fault diagnosis Fault detection Signal processing Frequency Wavelet packets Intelligent systems Kernel Machine intelligence |
| Content Type | Text |
| Resource Type | Article |
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