Cross Terms In Wigner-Ville Distribution Research Articles

Sliding Mode Singular Spectrum Analysis for the Elimination of Cross-Terms in Wigner–Ville Distribution

The Wigner–Ville distribution (WVD) is a signal processing approach to evaluate a high-resolution time–frequency representation (TFR) of a multi-component signal. The WVD of a multi-component signal produces unwanted cross-terms in the TFR. The elimination of these cross-terms using various signal processing techniques is a challenging research problem. In this paper, a data-driven signal decomposition technique is investigated for the elimination of these cross-terms in the WVD-based time–frequency representation of a multi-component signal. The approach is based on the decomposition of a multicomponent signal into its mono-components using sliding mode singular spectrum analysis (SM-SSA). The WVD of each mono-component is evaluated, and the sum of WVDs of all mono-components represents the cross-term free WVD representation of multi-component signals. Renyi entropy (RE) is used to quantify the performance of the proposed approach. Simulations are carried out using synthetic and real signals to verify the effectiveness of the proposed approach for the removal of cross-terms in WVD. The results demonstrated that SM-SSA has better performance with the lowest RE value as compared to other data-driven signal decomposition approaches such as automated SSA (AutoSSA) and swarm decomposition for the elimination of cross-terms from the WVD-based TFR of multi-component signals.

New method to suppress cross-terms of Wigner-Ville distribution

A new method was proposed to suppress the cross-terms of Wigner-Ville distribution.First the cross-terms were detected between the Wigner-Ville and the Margenau-Hill distributions based on the geometry difference of cross-terms representation then a mask filter was designed to eliminate them.Finally the results of simulation indicate that this technique is feasible.

Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution

Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine fault diagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis is the best-known technology applied in wind turbine condition monitoring, in which the time-frequency analysis techniques such as Wigner-Ville distribution (WVD) are widely used. Theoretically WVD has an infinite resolution in time-frequency domain. For early wind turbine fault signals, however, there are two main difficulties in WVD analysis. One is strong noise signals in the background and the other is cross terms in WVD itself. In this paper, continuous wavelet transformation (CWT) is employed to filter useless noise in raw vibration signals, and auto terms window (ATW) function is used to suppress the cross terms in WVD. In the CWT de-noising process, the Morlet wavelet, whose shape is similar to mechanical shock signals, is chosen to perform CWT on the raw vibration signals. The appropriate scale parameter for CWT is optimized by the cross validation method (CVM). An ATW based on the Smoothed Pseudo Wigner-Ville distribution (SPWVD) spectrum is taken to be a window function to suppress the cross terms in WVD. The new method can not only remove cross terms faraway from the auto terms, but also keep high energy close to every instantaneous frequency, the virtues such as high time-frequency resolution, and good energy aggregation etc. The wind turbine gear fault diagnosis experiment results indicate that the proposed method has a good de-nosing performance and is effective in suppressing the cross terms and extracting fault feature.

Using ASTFT Spectrum to Suppress Cross Terms in WVD and its Application in Fault Diagnosis

A new fault diagnosis method to suppress cross terms of Wigner-Ville distribution (WVD) using Adaptive Short-time Fourier Transform (ASTFT) spectrum is put forward. The relationships of correlation between auto terms and cross terms of WVD are obtained theoretically by analyzing the WVD. Firstly, the signal ASTFT spectrum which can determine the signal component positions in the time-frequency plane is obtained. Then, the ASTFT spectrum as a window function is selected to process the signal WVD. Thus the cross terms can be effectively restrained. The simulation results show that a better resolution and more effective suppression of cross terms can be obtained. At last, the proposed method is applied to the fault diagnosis of bearing. The simulation and the experiment results indicate that the proposed method is effective in feature extraction.