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Numerical simulation of vortex evolution based on adaptive wavelet method

Applied Mathematics and Mechanics(English Edition) 2011年第1期32卷 33-44页

作者：赵勇宗智邹文楠 School of Naval Architecture Faculty of Vehicle Engineering and MechanicsState Key Laboratory of Structural Analysis for Industrial EquipmentDalian University of Technology Institute of Engineering Mechanics Nanchang University

The application of the wavelet method to vortex motion prediction is investigated. First, the wavelet method is used to solve two initial boundary problems so as to verify its abilities of controlling numerical errors and capturing local structures. Then, the adaptive wavelet method is used to simulate the vortex emerging process. The results show that the wavelet method can control numerical errors easily, can capture local structures adaptively, and can predict the vortex fluctuation evolution. Therefore, the application of the wavelet method to turbulence is suggested.

关键词： adaptive wavelet vortex evolution coherent structure vorticity equation,turbulence

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adaptive wavelet-BASED NEURAL NETWORK FOR FAULT CLASSIFICATION IN BEARING

ADAPTIVE WAVELET-BASED NEURAL NETWORK FOR FAULT CLASSIFICATI...

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第七届海内外青年设计与制造科学会议

作者： Chen Wenge HuJiao Zhao and Xuezhi Faculty of Electromechanical Engineering Guangdong Univ. of Tech. China Mechanical Engineering College South China Univ. of Tech. China

＜正＞One kind of adaptive wavelet-based neural network for fault classification is put forward. The first component of network is used to extract signal’s feature through a family of wavelet’s decomposition for signal in different channel, the second component of network is used to learn these information or judge fault type according to these feature. The learning algorithm is presented in detail and this network is applied to the bearings’ fault classification. The result demonstrates that this neural network can classify fault accurately and reliably.

关键词： adaptive wavelet Neural Networks Bearings Fault Classification

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Incipient Gear Fault Detection Using adaptive Impulsive wavelet Filter Based on Spectral Negentropy

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Chinese Journal of Mechanical Engineering 2022年第1期35卷 156-178页

作者： Mang Gao Gang Yu Changning Li School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518055China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China

adaptive wavelet filtering is a very important fault feature extraction method in the domain of condition monitoring;however, owing to the time-consuming computation and difficulty of choosing criteria used to represent incipient faults, the engineering applications are limited to some extent. To detect incipient gear faults at a fast speed, a new criterion is proposed to optimize the parameters of the modified impulsive wavelet for constructing an optimal wavelet filter to detect impulsive gear faults. First, a new criterion based on spectral negentropy is proposed. Then, a novel search strategy is applied to optimize the parameters of the impulsive wavelet based on the new criterion. Finally,envelope spectral analysis is applied to determine the incipient fault characteristic frequency. Both the simulation and experimental validation demonstrated the superiority of the proposed approach.

关键词： Incipient fault diagnosis Negentropy Spectral kurtosis Gear adaptive wavelet

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维普期刊数据库

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The Research of Underwater Image De-noising Method Based on adaptive wavelet Transform

The Research of Underwater Image De-noising Method Based on ...

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第26届中国控制与决策会议

作者： Lei Fei Wang Yingying Academy of Electronic Information and Control Engineering Beijing University of Technology

ISBN: (纸本)9781479937097

For the problem of underwater image de-noising, a new method based on adaptive wavelet combining adaptive threshold selection with adaptive output of the threshold function is proposed. Considering the underwater image with low SNR, contrast imbalance, and poor image quality, first some pre-processing should be done before wavelet threshold de-nosing. Then,we adopt adaptive wavelet combining adaptive threshold selection with adaptive output of the threshold function for the image de-noising. Finally the simulation results show that the proposed method not only removes noise effectively, improves image output peak signal-to-noise ratio（PSNR）, but also yields superior vision quality and embodies the superiority of wavelet de-noising.

关键词： Image De-noising Threshold Underwater Image adaptive wavelet Peak Signal to Noise Ratio(PSNR)

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