Papers by Keyword: Wavelet Package Transform

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Authors: Qing Kai Han, Bang Chun Wen
Abstract: In grinding process, chatter vibrations known as self-excited vibrations, become increasingly problematic and must be avoided. Firstly, experimental measurements of chatter vibrations in a numerical control (NC) grinding machine tool are introduced for the case of a special alloy workpiece being carried on. Then, frequency spectra of chatter vibrations are analyzed. The wavelet package transform technique is used to describe original chatter signals in the term of scaled energy of frequency bands. At last, fractal dimensions of the reconstructed signals in consecutive frequency bands of chatter vibrations are calculated. These results are helpful for understanding of the nonlinearity of chatter vibrations.
Authors: Xue Li Shen, Ying Le Fan
Abstract: Research on automatic sleep staging based on EEG signals has a significant meaning for objective evaluation of sleep quality. An improved Hilbert-Huang transform method was applied to time-frequency analysis of non-stable EEG signals for the sleep staging in this paper. In order to settle the frequency overlapping problem of intrinsic mode function obtained from traditional HHT, wavelet package transform was introduced to bandwidth refinement of EEG before the empirical mode decomposition was conducted. This method improved the time-frequency resolution effectively. Then the intrinsic mode functions and their marginal spectrums would be calculated. Six common spectrum energies (or spectral energy ratios) were selected as characteristic parameters. Finally, a probabilistic nearest neighbor method for statistical pattern recognition was applied to optimal decision. The experiment data was from the Sleep-EDF database of MIT-BIH. The classification results showed that the automatic sleep staging decisions based on this method conformed roughly with the manual staging results and were better than those obtained from traditional HHT obviously. Therefore, the method in this paper could be applied to extract features of sleep stages and provided necessary dependence for automatic sleep staging.
Authors: Chen Quan Hua, Yan Feng Geng
Abstract: A novel noninvasive approach to the online flow regime identification for wet gas flow in a horizontal pipeline is proposed. Research into the flow-induced vibration response for the wet gas flow was conducted, with the conditions of pipe diameter 50 mm, pressure from 0.25 MPa to 0.35 MPa, Lockhart-Martinelli parameter from 0.02 to 0.6, and gas Froude Number from 0.5 to 2.7. The flow-induced vibration signals were measured by a vibration transducer installed by outside wall of pipe, and then the normalized energy features from different frequency bands in the vibration signals were extracted through 4-scale wavelet package transform (WPT) with mother wavelet db7. A probabilistic neural network (PNN) classifier with the extracted features as inputs was developed to identify the three typical flow regimes including stratified wavy flow, annular mist flow, and slug flow for wet gas flow. The results show that the method can identify effectively flow regimes and its identification accuracy arrives at above 92.1%. The noninvasive measurement approach has great application prospect in online flow regime identification.
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