Signature Features in Returned Echoes From Submerged Targets Insonified by Short, Broadband Pulses: Comparison of Experiments and Theory

1995 ◽  
Author(s):  
Torbjörn Ståhlsten ◽  
Hans C. Strifors ◽  
Guillermo C. Gaunaurd
Keyword(s):  
Experimental Data ◽  
Theoretical Prediction ◽  
Frequency Domain ◽  
Time Window ◽  
Wigner Distribution ◽  
Time Frequency ◽  
Comparison Of Experiments ◽  
Cross Terms ◽  
Sound Pulses ◽  
Broadband Sound

Abstract Backscattered echoes are studied from submerged elastic targets in the frequency domain and combined time-frequency domain when the targets are insonified by short, broadband sound pulses. The targets are either an air-filled spherical shell or various solid brass or steel spheres. The incident waveform is generated by weighting a sinusoidal signal with a Blackman time-window of a few cycles width. The spectrum is computed from each recorded set of experimental data and the result is shown to agree well with the theoretical prediction for the corresponding target and interrogating waveform. An advantage of the time-frequency approach is that target signatures can show the time evolution of the resonance features that identify each target. Experimentally obtained data are processed in the time-frequency domain using a pseudo-Wigner distribution (PWD). The associated time-window is Gaussian, and its width is adjusted to suppress the interference of cross-terms in the PWD, yet retaining the desired property of time-frequency concentrating the extracted features.

Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2831-2847 ◽  
Author(s):  
Y Zhou ◽  
J Chen ◽  
G M Dong ◽  
W B Xiao ◽  
Z Y Wang
Keyword(s):  
Fault Diagnosis ◽  
Spectral Density ◽  
Wigner Distribution ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Accurate Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Cross Terms

The vibration signals of rolling element bearings are random cyclostationary when they have faults. Also, statistical properties of the signals change periodically with time. The accurate analysis of time-varying signals is an essential pre-requisite for the fault diagnosis and hence safe operation of rolling element bearings. The Wigner distribution is probably most widely used among the Cohen’s class in order to describe how the spectral content of a signal changes over time. However, the basic nature of such signals causes significant interfering cross-terms, which do not permit a straightforward interpretation of the energy distribution. To overcome this difficulty, the Wigner–Ville distribution (WVD) based on the cyclic spectral density (CSD) is discussed in this article. It is shown that the improved WVD, based on CSD of a long time series, can render the time–frequency distribution less susceptible to noise, and restrain the cross-terms in the time–frequency domain. Simulation and experiment of the rolling element-bearing fault diagnosis are performed, and the results indicate the validity of WVD based on CSD in time–frequency analysis for bearing fault detection.

scholarly journals Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

2019 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bano ◽  
Martin Riera-Guasp ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Time Window ◽  
Early Stage ◽  
Induction Machines ◽  
Frequency Domain Analysis ◽  
Frequency Resolution ◽  
Good Time ◽  
Band Frequency ◽  
Time Frequency ◽  
Multi Band

Induction machines drive many industrial processes, and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, etc. In these cases an analysis in the time-frequency domain -such as a spectrogram- is required for detecting faults signatures. The spectrogram is built using the short frequency Fourier transform, but its resolution depends critically on the time window used to generate it: short windows provide good time resolution, but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper, this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms, and combines them into a single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

scholarly journals Comparative analysis of variants of Gabor-Wigner transform for cross-term reduction

2012 ◽  
Vol 19 (3) ◽  
pp. 499-508 ◽  
Author(s):  
Muhammad Ajab ◽  
Imtiaz Ahmad Taj ◽  
Nabeel Ali Khan
Keyword(s):  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Wigner Distribution ◽  
Gabor Transform ◽  
Wigner Transform ◽  
Cross Term ◽  
Time Frequency ◽  
Image Processing Techniques ◽  
Cross Terms ◽  
Processing Techniques

Abstract Gabor Wigner Transform (GWT) is a composition of two time-frequency planes (Gabor Transform (GT) and Wigner Distribution (WD)), and hence GWT takes the advantages of both transforms (high resolution of WD and cross-terms free GT). In multi-component signal analysis where GWT fails to extract auto-components, the marriage of signal processing and image processing techniques proved their potential to extract autocomponents. The proposed algorithm maintained the resolution of auto-components. This work also shows that the Fractional Fourier Transform (FRFT) domain is a powerful tool for signal analysis. Performance analysis of modified fractional GWT reveals that it provides a solution of cross-terms of WD and blurring of GT.

Time-Frequency Feature Extraction without Cross-Terms Based on Acoustic Signal in Rotor Malfunctions

2005 ◽  
Vol 293-294 ◽  
pp. 467-474 ◽  
Author(s):  
F.Q. Wu ◽  
Guang Meng
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Wigner Distribution ◽  
Component Analysis ◽  
Independent Component ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Cross Terms ◽  
The Cross

An effective approach is presented to eliminate the cross-terms in Wigner distribution by ICA (independent component analysis) and EMD (empirical mode decomposition), through which the cross-terms caused by the uncorrelated mixing signals can be removed successfully. This method is used for time-varying signal analysis and is powerful in signal feature extraction, especially for joint time frequency resolution, which is demonstrated by numerical examples. To further understand the method and its application, a detailed analysis about abrupt unbalance experimental example is shown to explain the cause of malfunction as well as its occurrence and phenomenon. In addition, the proposed approach based upon independent component analysis, empirical mode decomposition method and wigner distribution allows the separation and analysis of the sources with nonlinear and non-stationary properties. In this method, the main conceptual innovations are the associated introduction of ‘source separation’ and ‘intrinsic mode functions’ based on the local properties of the mixed signals, which makes the instantaneous frequency meaningful; the method serves to illustrate the roles played by the nonlinear and non-stationary effects in the energy-time-frequency distribution. At the same time, the method can also be expanded and applied in other fields.

scholarly journals Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3361 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Baño ◽  
Martin Riera-Guasp ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Time Window ◽  
Early Stage ◽  
Induction Machines ◽  
Frequency Domain Analysis ◽  
Frequency Resolution ◽  
Good Time ◽  
Band Frequency ◽  
Time Frequency ◽  
Multi Band

Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain—such as a spectrogram—is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it—short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

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