Local damage diagnosis in gearboxes using novel wavelet technology

Gryllias, Konstantinos; Gelman, Leonid M.; Shaw, Brian; Vaidhianathasamy, M

doi:10.1784/insi.2010.52.8.437

Cited by 15 publications

(19 citation statements)

References 21 publications

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“…To achieve optimal effectiveness of the detection of transients caused by a tooth fault, the width of the wavelet should match the length of these transients. Therefore, the t B f c product defining the length of the wavelet should be set for every scale a = f c / f (f is a Morlet wavelet frequency) in the way that the time-width at B = t B f c / f of the analysing wavelet matches the meshing period T m [8,9,10] .…”

Section: Vibration Diagnosismentioning

confidence: 99%

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Vibration diagnosis of a gearbox by wavelet bicoherence technology

Gelman

Solinski

Shaw

et al. 2017

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Gearboxes are critical elements of mechanical systems that are widely used in aerospace, energy generation, land and naval applications. The early detection of changes in the technical condition of this equipment is of great importance for the optimisation of maintenance costs. Vibration signal components resulting from the presence of the developing faults of meshing gears contain the information that, once extracted from the signal, may allow for a reliable estimation of the technical condition of the meshing gears. Wavelet bicoherence (WB)-based technology has been used to obtain the signal feature characterising the phase relationship between the signal components generated by gear faults in the selected frequency bandwidths. In previous research, WB has been successfully applied to the detection of artificiallycreated gearbox faults. This paper will present the application of WB in the detection of naturally-developing gear faults.

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Section: Vibration Diagnosismentioning

confidence: 99%

“…The transient non-stationary character of vibration generated by gear faults, which makes them difficult to capture with FFTbased methods, makes the wavelet transform, which preserves the temporal information, a particularly useful tool that has been successfully used for vibration transient detection [3][4][5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

Vibration diagnosis of a gearbox by wavelet bicoherence technology

Gelman

Solinski

Shaw

et al. 2017

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“…where 〈…〉 T stands for the local time averaging operator and W Ψ is the continuous wavelet transform of signal x(t) in the form [8] :…”

Section: Estimation Of the Wavelet Bicoherence Featurementioning

confidence: 99%

Diagnosis of bearings by novel non-linear non-stationary higher order spectra

Gelman¹,

Murray²,

Patel³

et al. 2013

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“…Liu et al [6] used diagnostic features from the autocorrelation spectrum of the integrated cross-correlation sequence of the normalised wavelet coefficients. The diagnostics proposed in [7,8] used a feature in the form of a wavelet modulus integrated over and normalised by frequency bands. The frequency bands were either identified from the wavelet scalograms [7] or by using spectral kurtosis [8] .…”

Section: Introductionmentioning

confidence: 99%

“…The diagnostics proposed in [7,8] used a feature in the form of a wavelet modulus integrated over and normalised by frequency bands. The frequency bands were either identified from the wavelet scalograms [7] or by using spectral kurtosis [8] . It has been shown [8] that the use of spectral kurtosis, in comparison to wavelet transform, for frequency band selection offers a better diagnostic effectiveness.…”

Section: Introductionmentioning

confidence: 99%