The Wigner-Ville distribution （WVD） and the cross Wigner-Ville distribution （XWVD） have been shown to be efficient in the estimation of instantaneous frequency （IF）. But the statistical result of the IF estimati...The Wigner-Ville distribution （WVD） and the cross Wigner-Ville distribution （XWVD） have been shown to be efficient in the estimation of instantaneous frequency （IF）. But the statistical result of the IF estimation from XWVD peak is much better than using WVD peak. The reason is given from a statistical point of view. Theoretical studies show that XWVD of the analyzed signal can be estimated from XWVD of the noise-contaminated signal. The estimation is unbiased, and the variance is equal to that of noise. In this case, WVD cannot be estimated from W-VD of the noise-contaminated signal. Therefore, higher SNR is required when WVD is used to analyze signals.展开更多
In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption...In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.展开更多
To detect higher order polynomial phase signals （HOPPSs）, the smoothed-pseudo polynomial Wigner-Ville distribution （SP-PVCVD）, an improved version of the polynomial Wigner-Ville distribution （PVCVD）, is presente...To detect higher order polynomial phase signals （HOPPSs）, the smoothed-pseudo polynomial Wigner-Ville distribution （SP-PVCVD）, an improved version of the polynomial Wigner-Ville distribution （PVCVD）, is presented using a separable kernel. By adjusting the lengths of the functions in the kernel, the balance between resolution retaining and interference suppressing can be adjusted conveniently. The proposed method with merits of interference terms reduction and noise suppression can provide time frequency representation of better readability and more accurate instantaneous frequency （IF） estimation with higher order SP-PVfVD. The performance of the SP-PWVD is verified by computer simulations.展开更多
This paper analyzes statistical performances of the Wigner-Ville distribution (WVD) and the cross Wigner-Ville distribution (XWVD). Theoretical studies show that XWVD of the analyzed signal can be estimated from XWVD ...This paper analyzes statistical performances of the Wigner-Ville distribution (WVD) and the cross Wigner-Ville distribution (XWVD). Theoretical studies show that XWVD of the analyzed signal can be estimated from XWVD of the noise-contaminated signal . The estimation is unbiased, and the variance is equal to that of noise. In this case, WVD cannot be estimated from WVD of the noise-contaminated signal . Therefore higher SNR is required when using WVD.展开更多
Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio(SNR),wavelet characteristics of Wigner-Ville distribution are applied to sort unknown complicated radar signal,high sorting ac...Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio(SNR),wavelet characteristics of Wigner-Ville distribution are applied to sort unknown complicated radar signal,high sorting accuracy can be got.The Wigner-Ville distribution of received signal is calculated,then it is predigested to two-dimensional characteristics.Using wavelet transformation to extract characteristics from two-dimensional of Wigner-Ville distribution,the best characteristics are selected to be used as sorting parameters.Experiment results demonstrated that the characteristics of eight typical radar emitter signals extracted by this method showed good performance of noise-resistance and clustering at large-scale SNR.展开更多
基金the National Natural Science Foundation of China (60472102)Shanghai Leading Academic Discipline Project (T0103)the Foundation of Shanghai Municipal Commission of Education (A10-0109-06-022)
文摘The Wigner-Ville distribution （WVD） and the cross Wigner-Ville distribution （XWVD） have been shown to be efficient in the estimation of instantaneous frequency （IF）. But the statistical result of the IF estimation from XWVD peak is much better than using WVD peak. The reason is given from a statistical point of view. Theoretical studies show that XWVD of the analyzed signal can be estimated from XWVD of the noise-contaminated signal. The estimation is unbiased, and the variance is equal to that of noise. In this case, WVD cannot be estimated from W-VD of the noise-contaminated signal. Therefore, higher SNR is required when WVD is used to analyze signals.
基金supported by National Natural Science Foundation of China (Grant No. 50875016, 51075023)Fundamental Research Funds for the Central Universities of China (Grant No. JD0903, JD0904)
文摘In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.
基金supported partly by the Program for New Century Excellent Talents in University, Ministry of Education,China(NCET-05-0803)supported by Information Controlling Technology of Communication System National Key Laboratory(9140C1301020801).
文摘To detect higher order polynomial phase signals （HOPPSs）, the smoothed-pseudo polynomial Wigner-Ville distribution （SP-PVCVD）, an improved version of the polynomial Wigner-Ville distribution （PVCVD）, is presented using a separable kernel. By adjusting the lengths of the functions in the kernel, the balance between resolution retaining and interference suppressing can be adjusted conveniently. The proposed method with merits of interference terms reduction and noise suppression can provide time frequency representation of better readability and more accurate instantaneous frequency （IF） estimation with higher order SP-PVfVD. The performance of the SP-PWVD is verified by computer simulations.
文摘This paper analyzes statistical performances of the Wigner-Ville distribution (WVD) and the cross Wigner-Ville distribution (XWVD). Theoretical studies show that XWVD of the analyzed signal can be estimated from XWVD of the noise-contaminated signal . The estimation is unbiased, and the variance is equal to that of noise. In this case, WVD cannot be estimated from WVD of the noise-contaminated signal . Therefore higher SNR is required when using WVD.
基金Supported by the National Science and Technology Supported Program of China(No.2011BAH24B05)
文摘Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio(SNR),wavelet characteristics of Wigner-Ville distribution are applied to sort unknown complicated radar signal,high sorting accuracy can be got.The Wigner-Ville distribution of received signal is calculated,then it is predigested to two-dimensional characteristics.Using wavelet transformation to extract characteristics from two-dimensional of Wigner-Ville distribution,the best characteristics are selected to be used as sorting parameters.Experiment results demonstrated that the characteristics of eight typical radar emitter signals extracted by this method showed good performance of noise-resistance and clustering at large-scale SNR.
