Pseudo Wigner–Ville Time-Frequency Distribution and Its Application to Machinery Condition Monitoring

Shin, Young Sik; Jeon, Jae Hyun

doi:10.1155/1993/372086

Cited by 65 publications

(39 citation statements)

References 8 publications

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“…An alternative to STFT is the Wigner Ville distribution. This distribution and its various permutations have been used by various authors [16,17] to detect the presence of local gear faults by means of vibration signal analysis. The Wigner distribution is derived by generalizing the relationship between the power spectrum and the autocorrelation function for non-stationary time-variant processes.…”

Section: Wigner-ville Time-frequency Analysismentioning

confidence: 99%

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Gearbox Vibration Signal Amplitude and Frequency Modulation

Chaari

Bartelmus

Zimroz

et al. 2012

Shock and Vibration

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Abstract. Gearboxes usually run under fluctuating load conditions during service, however most of papers available in the literature describe models of gearboxes under stationary load conditions. Main task of published papers is fault modeling for their detection. Considering real situation from industry, the assumption of stationarity of load conditions cannot be longer kept. Vibration signals issued from monitoring in maintenance operations differ from mentioned models (due to load non-stationarity) and may be difficult to analyze which lead to erroneous diagnosis of the system. The objective of this paper is to study the influence of time varying load conditions on a gearbox dynamic behavior. To investigate this, a simple spur gear system without defects is modeled. It is subjected to a time varying load. The speed-torque characteristic of the driving motor is considered. The load variation induces speed variation, which causes a variation in the gearmesh stiffness period. Computer simulation shows deep amplitude modulations with sidebands that don't differ from those obtained when there is a defective tooth. In order to put in evidence the time varying load effects, Short Time Fourier Transform and then Smoothed Wigner-Ville distribution are used. Results show that the last one is well suited for the studied case.The experimental validation presented at the end of the paper confirms the obtained results. Such results offer useful information when diagnosing gear transmissions by avoiding confusing conclusions from vibration signals.

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Section: Wigner-ville Time-frequency Analysismentioning

confidence: 99%

“…A frequency-smoothing window is applied to this distribution in order to attenuate the interference and cross-terms that are its inherent property of the distribution. This is performed by Smoothed Wigner-Ville Distribution (SWVD) [17]. It is defined for a time signal s(t) by:…”

Section: Wigner-ville Time-frequency Analysismentioning

confidence: 99%

Gearbox Vibration Signal Amplitude and Frequency Modulation

Chaari

Bartelmus

Zimroz

et al. 2012

Shock and Vibration

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“…Others use time and frequency method combined with statistical approach [5][6][7][8][9][10], which provides very good comparisons in between present and past vibrations and a definite indication for damages in the system. In addition, the use of joint time-frequency domain methods based on the Wigner-Ville Distribution (WVD) as well as the Continuous Wavelet Transform (CWT) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] have also been applied extensively to detect gear and bearing failures in transmission systems. The joint time-frequency domain methods provide an instantaneous frequency spectrum of the system at various time instances of the rotor rotation and can be used to pinpoint accurately the location of the damage in a gear transmission system.…”

Section: Introductionmentioning

confidence: 99%

Vibration Signature Analysis and Parameter Extractions on Damages in Gears and Rolling Element Bearings

Shen

Wen

Arunyanart

et al. 2011

ISRN Mechanical Engineering

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This paper is to analyze and identify damage in gear teeth and rolling element bearings by establishing pattern feature parameters from vibration signatures. In the present work, different damage scenarios involving different combinations of gear tooth damage, bearing damage are considered. Each of the damage scenarios are studied and compared in the time domain, the frequency domain, and the joint time-frequency domain using the FM0 technique, the Fourier Transform, the Wigner-Ville Transform, and the Continuous Wavelet Transform, respectively. Results obtained from the three different signal domains are analyzed to develop indicative parameters and visual presentations that measure the integrity and wellness of the bearing and gear components. The joint time-frequency domain obtained from the continuous wavelet transform has shown to be a superior technique for providing clear visual examination solution for different types of component damages as well as for feature extractions used for computerbased machine health monitoring solution.

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“…In the case of signals with multiple frequency components, the Wigner}Ville distribution is very complicated and di$cult to interpret due to the interference e!ect [14]. Also, unless the signal x(t) is a bandlimited signal, it is not possible to calculate the Wigner}Ville distribution in practice, as it requires integration over all time or all frequency [8].…”

Section: Introductionmentioning

confidence: 99%

“…Also, unless the signal x(t) is a bandlimited signal, it is not possible to calculate the Wigner}Ville distribution in practice, as it requires integration over all time or all frequency [8]. Due to these problems, weighting windows are applied to data arrays of each shift in both the time and frequency domains, which leads to the formula for the PWVD [8,14] PWVD(x(t, ))"h( )…”

Section: Introductionmentioning

confidence: 99%