Diagnosis of tooth surface failure by wavelet transform of dynamic characteristics

Yoshida, Akira; Ohue, Yuji; Ishikawa, Hiroshi

doi:10.1016/s0301-679x(00)00042-6

Cited by 23 publications

(11 citation statements)

References 4 publications

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“…In this paper we use the name, standard Morlet wavelet, for this simpli"ed form and complete Morlet wavelet, for the complete form given by equation (13). The standard Morlet wavelet is simply a complex sinusoid within a Gaussian envelope, where the central frequency, M , in e!ect determines the number of signi"cant oscillations of the complex sinusoid within the Gaussian window.…”

Section: The Morlet Waveletmentioning

confidence: 99%

Low-Oscillation Complex Wavelets

Addison

Watson

Tian³

2002

Journal of Sound and Vibration

113

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Section: The Morlet Waveletmentioning

confidence: 99%

Low-Oscillation Complex Wavelets

Addison

Watson

Tian³

2002

Journal of Sound and Vibration

113

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“…Yoshida, et al (12) discussed the dynamic characteristics of the gear mesh due to changes in the tooth profile during the fatigue test using Gabor-based WTs. A fatigue test of the gear was done on a back-to-back power recirculation gear testing machine.…”

Section: Introductionmentioning

confidence: 99%

Surface Contact Fatigue Failure Assessment in Spur Gears Using Lubricant Film Thickness and Vibration Signal Analysis

Amarnath

Sujatha

2015

Tribology Transactions

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Gears are very common machine elements that are used to transmit power. During power transmission, the gear teeth are subjected to high contact pressure, leading to wear and tear, even though adequate lubrication is used. When gears operate near their maximum load capacity, very high contact pressure occurs at the mesh interference. This may lead to partial breakdown of the lubricant film at the gear teeth contact surface, thereby triggering distributed faults on gear teeth, viz. scuffing, scoring, spalling, pitting, and mild wear. Currently there are three different approaches to the detection of faults in geared systems: vibration analysis, oil/wear particle analysis, and acoustic signal analysis. Generally, in an industrial environment, vibration analysis and oil/ wear particle analyses are conducted independently, but of late there are requirements in the diagnostic world for more reliable and effective diagnostic information regarding fault growth in machinery components. In the present work, experimental investigations are carried out to assess surface fatigue wear on spur gear teeth of back-to-back power recirculation-type spur gearbox; in particular, vibration signal analysis using continuous wavelet transform is used in conjunction with specific film thickness studies. Specific film thickness analysis provided details of lubrication regimes in gear teeth contacts and corresponding wear propagation on gear teeth, and the vibration signals acquired from a gear pair are postprocessed using Morlet wavelet transform. Results obtained from the integration of specific film thickness analysis and wavelet transform provide better diagnostic information than the lubricant film thickness estimation alone for detection of gear faults.

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“…This is contrary to the spectrum, which accommodates only a global vision. In its continuous version, the wavelet analysis has been applied by several researchers to this problem [15][16][17][18]. Discrete analysis (or multi-resolution) has been also used to extract the defect signature from the globally measured signal.…”

Section: Introductionmentioning

confidence: 99%

Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects

et al. 2012

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Publication informationMeccanica, 47 (7) Abstract -In machine defect detection, namely those of gears, the major problem is isolating the defect signature from the measured signal, especially where there is significant background noise or multiple machine components. This article presents a method of gear defect detection based on the combination of Wavelet Multi-resolution Analysis and the Hilbert transform. The pairing of these techniques allows simultaneous filtering and denoising, along with the possibility of detecting transitory phenomena, as well as a demodulation. This paper presents a numerical simulation of the requisite mathematical model followed by its experimental application of acceleration signals measured on defective gears on a laboratory test rig. Signals were collected under various gear operating conditions, including defect size, rotational speed, and frequency bandwidth. The proposed method compares favourably to commonly used analysis tools, with the advantage of enabling defect frequency isolation, thereby allowing detection of even small or combined defects.

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Diagnosis of tooth surface failure by wavelet transform of dynamic characteristics

Cited by 23 publications

References 4 publications

Low-Oscillation Complex Wavelets

Low-Oscillation Complex Wavelets

Surface Contact Fatigue Failure Assessment in Spur Gears Using Lubricant Film Thickness and Vibration Signal Analysis

Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects

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