Novel detection of local tooth damage in gears by the wavelet bicoherence

Combet, François; Gelman, Len; LaPayne, G.

doi:10.1016/j.ymssp.2011.07.002

Cited by 35 publications

(39 citation statements)

References 17 publications

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“…Conventional wavelet spectrum based WB is incapable of eliminating the spurious peaks coming from long coherence time waves and non-QPC waves [17,19]. As a result, direct application may cause incorrect results.…”

Section: Wavelet Bicoherence Based Quadratic Nonlinearity Featurementioning

confidence: 99%

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Wang

Lin

2014

2014 International Conference on Prognostics and Health Management

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Unexpected bearing failures may cause unscheduled downtime and economic losses. It is, therefore, very important to find the faults symptoms of the rolling element bearing components. Vibration signal of fault bearing is nonlinear and non-stationary in nature, which makes the stationary assumed methods not appropriate. In this paper, a biphase randomization wavelet bicoherence method is introduced, which combines benefits of the wavelet transform and the bicoherence analysis. By simultaneously using the amplitude of the continuous wavelet transform and biphase information, this method can eliminate the spurious bicoherence coming from long coherence time waves and non phase coupling waves efficiently. Based on this method, two quadratic nonlinearity features are proposed for fault diagnosis of rolling element bearing. At the same time, the proposed features are applied to the real-world vibration data collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Experiment results demonstrate that the performance of the proposed features is much better than that of some original features.

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Section: Wavelet Bicoherence Based Quadratic Nonlinearity Featurementioning

confidence: 99%

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Wang

Lin

2014

2014 International Conference on Prognostics and Health Management

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“…The S transform combines the separate strengths of the STFT and wavelet transforms [27] and has provided an alternative approach to process the non-stationary signals generated by mechanical systems [28].…”

Section: S Transformmentioning

confidence: 99%

Application of the DC Offset Cancellation Method and S Transform to Gearbox Fault Diagnosis

Zhang¹,

Zhao²,

Bajrić

et al. 2017

Applied Sciences

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Abstract:In this paper, the direct current (DC) offset cancellation and S transform-based diagnosis method is verified using three case studies. For DC offset cancellation, correlated kurtosis (CK) is used instead of the cross-correlation coefficient in order to determine the optimal iteration number. Compared to the cross-correlation coefficient, CK enhances the DC offset cancellation ability enormously because of its excellent periodic impulse signal detection ability. Here, it has been proven experimentally that it can effectively diagnose the implanted bearing fault. However, the proposed method is less effective in the case of simultaneously present bearing and gear faults, especially for extremely weak bearing faults. In this circumstance, the iteration number of DC offset cancellation is determined directly by the high-speed shaft gear mesh frequency order. For the planetary gearbox, the application of the proposed method differs from the fixed-axis gearbox, because of its complex structure. For those small fault frequency parts, such as planet gear and ring gear, the DC offset cancellation's ability is less effective than for the fixed-axis gearbox. In these studies, the S transform is used to display the time-frequency characteristics of the DC offset cancellation processed results; the performances are evaluated, and the discussions are given. The fault information can be more easily observed in the time-frequency contour than the frequency domain.

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“…5 It was proved that the early fault detection on crack propagation was possible by AE. Combet, et al 13 studied two different waveform methods to obtain local and instantaneous averages of the signals. Local faults were recognized, and they found a diagnosing method dependent on the angle at a special frequency range.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on Gear Diagnosis by Using Acoustic Emission Technique

Ulus¹,

Erkaya²

2016

IJAV

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Acoustic Emission (AE) is one of the condition monitoring and diagnosing techniques of rotating machine elements such as gears, bearings, etc. So far, many studies about fault diagnosis on gearboxes have been implemented for vibration monitoring. In addition, a great deal of research on spur gears has been done for understanding the possible gear faults by considering their acoustic characteristics. In this study, possible faults in gears were analysed by the AE technique. A single-stage gearbox system comprising both helical and spur gears was used to identify the existence of possible gear faults, such as pitting and cracking at the tooth root. Noise signal in time-domain is converted to frequency-domain by using Fast Fourier Transform (FFT). In the experimental stage, artificial faults were implemented, and some mathematical parameters such as Root Mean Square error (RMS), Crest Factor (CF), and maximum value of noise level were considered to identify the fault occurrence at the meshing gear. The results show that the AE technique is very effective in diagnosing the defects in a gear system by a contactless measurement. Also, compared to the other diagnostic approaches, it is clear that the gear defects can be determined at an earlier stage by the AE technique.

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Novel detection of local tooth damage in gears by the wavelet bicoherence

Cited by 35 publications

References 17 publications

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features

Application of the DC Offset Cancellation Method and S Transform to Gearbox Fault Diagnosis

An Experimental Study on Gear Diagnosis by Using Acoustic Emission Technique

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