The Experts below are selected from a list of 6678 Experts worldwide ranked by ideXlab platform

Baoping Tang - One of the best experts on this subject based on the ideXlab platform.

  • feature extraction method of wind turbine based on adaptive Morlet Wavelet and svd
    Renewable Energy, 2011
    Co-Authors: Yong Hua Jiang, Baoping Tang
    Abstract:

    Analyzing the vibration signals of wind turbine usually requires feature extraction. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. In this paper, a new denoising method based on adaptive Morlet Wavelet and singular value decomposition (SVD) is applied to feature extraction for wind turbine vibration signals. Modified Shannon Wavelet entropy is utilized to optimize central frequency and bandwidth parameter of the Morlet Wavelet so as to achieve optimal match with the impulsive components. The time-frequency resolution can be adapted to different signals of interest. Then, an improved matrix construction method is used to construct matrix of the Wavelet coefficient, and the scale periodical exponential (SPE) spectrum is obtained by SVD for selecting the appropriate transform scale. Experimental analysis and application into signal denoising indicate that the proposed method has better denoising performance than other Wavelet transforms. The results of the experimental analysis in rolling bearing and the application in planetary gearbox show that the proposed method is an effective approach to detecting the impulsive feature components hidden in vibration signals and performs well for wind turbine fault diagnosis.

  • A hybrid time-frequency method based on improved Morlet Wavelet and auto terms window
    Expert Systems With Applications, 2011
    Co-Authors: Baoping Tang
    Abstract:

    Research highlights? The hybrid time-frequency method is based on Morlet Wavelet and auto terms window. ? The parameters are optimized by cross validation method and minimum Shannon entropy method. ? The useless noise in the original signal can be filtered by the CWT filter de-noising process. ? The cross terms in Wigner-Ville Distribution can be suppressed by ATW. In this paper, a hybrid time-frequency method (HTM) based on the improved Morlet Wavelet and auto terms window (ATW) is presented. The Morlet Wavelet, for its shape is similar to the mechanical shock signals, is added two parameters which decide the shape of the mother Wavelet. The added parameters and the appropriate scale parameter for continuous Wavelet transformation (CWT) are calculated using the cross validation method (CVM) and the minimum Shannon entropy method. The useless noise in the original signal can be filtered by the CWT filter de-noising process. An ATW based on the Smoothed Pseudo Wigner-Ville Distribution (SPWVD) spectrum is designed as a window function to suppress the cross terms in Wigner-Ville Distribution (WVD). The gear fault diagnosis experiment results show that the proposed method has a good de-nosing performance and is effective in removing the cross terms and extracting fault feature.

Yong Hua Jiang - One of the best experts on this subject based on the ideXlab platform.

  • feature extraction method of wind turbine based on adaptive Morlet Wavelet and svd
    Renewable Energy, 2011
    Co-Authors: Yong Hua Jiang, Baoping Tang
    Abstract:

    Analyzing the vibration signals of wind turbine usually requires feature extraction. However, in many cases, to extract feature components becomes challenging and the applicability of information drops down due to the large amount of noise. In this paper, a new denoising method based on adaptive Morlet Wavelet and singular value decomposition (SVD) is applied to feature extraction for wind turbine vibration signals. Modified Shannon Wavelet entropy is utilized to optimize central frequency and bandwidth parameter of the Morlet Wavelet so as to achieve optimal match with the impulsive components. The time-frequency resolution can be adapted to different signals of interest. Then, an improved matrix construction method is used to construct matrix of the Wavelet coefficient, and the scale periodical exponential (SPE) spectrum is obtained by SVD for selecting the appropriate transform scale. Experimental analysis and application into signal denoising indicate that the proposed method has better denoising performance than other Wavelet transforms. The results of the experimental analysis in rolling bearing and the application in planetary gearbox show that the proposed method is an effective approach to detecting the impulsive feature components hidden in vibration signals and performs well for wind turbine fault diagnosis.

  • Study on Modal Parameters Identification Based on Stratified Sampling and Complex Morlet Wavelet Transform
    Advanced Materials Research, 2011
    Co-Authors: Yong Hua Jiang, Hong Xu, Guang Ming Cheng
    Abstract:

    The natural frequency of large engineering structures are very low and closely, and it’s very difficult to excite the structures by exciter, in order to identify the modal parameters of large engineering structures, a novel modal parameters identification method based on stratified sampling and complex Morlet Wavelet transform is proposed. In order to improve the precision of sampling, stratified sampling, which replaces the random sampling, is applied on random decrement method for extracting the free decrement response signal, and a method is introduced to determine the sample layer weights based on fitting deviation and sample size. In order to improve the identification precision of closely spaced modals, a method is developed to adaptive select the bandwidth parameter and scale parameter of the Morlet Wavelet based on the principle of minimum Wavelet energy entropy and maximum energy. The analysis of data from the model test of Chongqing Chaotianmen bridge show that, the method is effective to identify the low and closely modal parameters.

Weiguo Huang - One of the best experts on this subject based on the ideXlab platform.

  • adaptive spectral kurtosis filtering based on Morlet Wavelet and its application for signal transients detection
    Signal Processing, 2014
    Co-Authors: Weiguo Huang, Shi-bin Wang
    Abstract:

    Spectral kurtosis (SK) provides a valuable tool for detecting the signal transients buried in noise, which makes it very powerful for designing a filter to extract the signal transients. However, SK requires the selection of a time-frequency frame for decomposition based on Short Time Fourier Transform (STFT). This paper presents an adaptive spectral kurtosis filtering technique to extract the signal transients based on Morlet Wavelet. The Morlet Wavelet is used as a filter bank whose center frequency is defined by the Wavelet correlation filtering. Different bandwidth filter in the filter bank is used to select the optimal filter for extracting the signal transients as the one that maximizes the SK. Effectiveness of the proposed technique is verified through the transient extraction of a simulate signal. For the gear fault feature detection of vehicle transmission gearbox, the proposed technique is applied in the extraction of the signal transients that shows the gear fault, which proves the effectiveness of the proposed technique in extracting the signal transients in the practical application.

  • adaptive parameter identification based on Morlet Wavelet and application in gearbox fault feature detection
    EURASIP Journal on Advances in Signal Processing, 2010
    Co-Authors: Shi-bin Wang, Yingping He, Weiguo Huang
    Abstract:

    Localized defects in rotating mechanical parts tend to result in impulse response in vibration signal, which contain important information about system dynamics being analyzed. Thus, parameter identification of impulse response provides a potential approach for localized fault diagnosis. A method combining the Morlet Wavelet and correlation filtering, named Cyclic Morlet Wavelet Correlation Filtering (CMWCF), is proposed for identifying both parameters of impulse response and the cyclic period between adjacent impulses. Simulation study concerning cyclic impulse response signal with different SNR shows that CMWCF is effective in identifying the impulse response parameters and the cyclic period. Applications in parameter identification of gearbox vibration signal for localized fault diagnosis show that CMWCF is effective in identifying the parameters and thus provides a feature detection method for gearbox fault diagnosis.

Jean-pierre Antoine - One of the best experts on this subject based on the ideXlab platform.

  • EUSIPCO - Analyzing NMR spectra with the Morlet Wavelet
    2008
    Co-Authors: Aimamorn Suvichakorn, Jean-pierre Antoine
    Abstract:

    We study the time-scale representation provided by the Morlet Wavelet transform for characterizing NMR signals. From an analytical analysis and simulations, we conclude that the Wavelet shows a satisfactory performance even when a baseline, an additive Gaussian noise or a solvent are present in the signals. It can also cope with non-Lorentzian lineshapes which commonly occur because of the inhomogeneous distribution of molecules in a substance. These results mean that the Morlet Wavelet transform is a potential tool to quantify in vivo NMR signals.

  • Analyzing NMR spectra with the Morlet Wavelet
    2008 16th European Signal Processing Conference, 2008
    Co-Authors: Aimamorn Suvichakorn, Jean-pierre Antoine
    Abstract:

    We study the time-scale representation provided by the Morlet Wavelet transform for characterizing NMR signals. From an analytical analysis and simulations, we conclude that the Wavelet shows a satisfactory performance even when a baseline, an additive Gaussian noise or a solvent are present in the signals. It can also cope with non-Lorentzian lineshapes which commonly occur because of the inhomogeneous distribution of molecules in a substance. These results mean that the Morlet Wavelet transform is a potential tool to quantify in vivo NMR signals.

  • Quantification method using the Morlet Wavelet for Magnetic Resonance Spectroscopic signals with macromolecular contamination
    2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
    Co-Authors: Aimamorn Suvichakorn, Helene Ratiney, Adriana Bucur, Sophie Cavassila, Jean-pierre Antoine
    Abstract:

    We study the Morlet Wavelet transform on characterizing Magnetic Resonance Spectroscopic (MRS) signals acquired at short echo-time. These signals contain contributions from metabolites, water and a baseline which mainly originates from large molecules, known as macromolecules, and lipids. The baseline signal decays faster than the metabolite ones. Therefore, by making use of the time-scale representation of the Wavelet, the two signals can be distinguished without any additional pre-processing. This is confirmed by the experimental results which show that the Morlet Wavelet can correctly quantify the metabolite contributions even when a baseline is embedded in the MRS signals.

Chenglong Yu - One of the best experts on this subject based on the ideXlab platform.