The characteristics of typical AE signals initiated by mechanical component damages are analyzed. Based on the extracting principle of acoustic emission(AE) signals from damaged components,the paper introduces Wigner ...The characteristics of typical AE signals initiated by mechanical component damages are analyzed. Based on the extracting principle of acoustic emission(AE) signals from damaged components,the paper introduces Wigner high-order spectra to the field of feature extraction and fault diagnosis of AE signals. Some main performances of Wigner binary spectra,Wigner triple spectra and Wigner-Ville distribution (WVD) are discussed,including of time-frequency resolution,energy accumulation,reduction of crossing items and noise elimination. Wigner triple spectra is employed to the fault diagnosis of rolling bearings with AE techniques. The fault features reading from experimental data analysis are clear,accurate and intuitionistic. The validity and accuracy of Wigner high-order spectra methods proposed agree quite well with simulation results. Simulation and research results indicate that wigner high-order spectra is quite useful for condition monitoring and fault diagnosis in conjunction with AE technique,and has very important research and application values in feature extraction and faults diagnosis based on AE signals due to mechanical component damages.展开更多
An algorithm for estimating the cross-bispectrum of an acoustic vector signal was formulated. Composed features of sound pressure and acoustic vector signals are extracted by the proposed algorithm and other estimatin...An algorithm for estimating the cross-bispectrum of an acoustic vector signal was formulated. Composed features of sound pressure and acoustic vector signals are extracted by the proposed algorithm and other estimating algorithms for secondary and higher order spectra. Its effectiveness was tested with lake and sea trial data. These features can be used to construct an input vector set for a radial basis function neural network. The classification of vessels can then be made based on the extracted features. It was shown that the composed features of acoustic vector signals are more easily divided into categories than those of pressure signals. When using the composed features of acoustic vector signals, the recognition rate of underwater acoustic targets improves.展开更多
The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of unde...The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.展开更多
基金Supported by the Project of Hunan Provincial Science and Technology Research (2007FJ3025)
文摘The characteristics of typical AE signals initiated by mechanical component damages are analyzed. Based on the extracting principle of acoustic emission(AE) signals from damaged components,the paper introduces Wigner high-order spectra to the field of feature extraction and fault diagnosis of AE signals. Some main performances of Wigner binary spectra,Wigner triple spectra and Wigner-Ville distribution (WVD) are discussed,including of time-frequency resolution,energy accumulation,reduction of crossing items and noise elimination. Wigner triple spectra is employed to the fault diagnosis of rolling bearings with AE techniques. The fault features reading from experimental data analysis are clear,accurate and intuitionistic. The validity and accuracy of Wigner high-order spectra methods proposed agree quite well with simulation results. Simulation and research results indicate that wigner high-order spectra is quite useful for condition monitoring and fault diagnosis in conjunction with AE technique,and has very important research and application values in feature extraction and faults diagnosis based on AE signals due to mechanical component damages.
基金Supported by the National Natural Science Foundation under Grant No.40827003
文摘An algorithm for estimating the cross-bispectrum of an acoustic vector signal was formulated. Composed features of sound pressure and acoustic vector signals are extracted by the proposed algorithm and other estimating algorithms for secondary and higher order spectra. Its effectiveness was tested with lake and sea trial data. These features can be used to construct an input vector set for a radial basis function neural network. The classification of vessels can then be made based on the extracted features. It was shown that the composed features of acoustic vector signals are more easily divided into categories than those of pressure signals. When using the composed features of acoustic vector signals, the recognition rate of underwater acoustic targets improves.
基金Supported by project of Natural Science Foundation of China(No.41174097)
文摘The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.
