Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect

Parey, Anand; Badaoui, Mohamed El; Guillet, François; Tandon, N.

doi:10.1016/j.jsv.2005.11.021

Cited by 174 publications

(102 citation statements)

References 20 publications

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“…^signal takes a value of 126.8, where A signal and A" (1) are the root mean square (RMS) amplitude for the signal and the noise, respectively.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Various time domain statistical parameters can be used as trend parameters to detect the presence of damage [1]. The time synchronous average (TSA) providing an average time signal of one individual gear over a large number of cycles has also been acknowledged as a powerful and very successful tool in the detection of gear faults [2,3] since it can remove the background noise and all the periodic events that are not exactly synchronous with the gear of interest.…”

Section: Introductionmentioning

confidence: 99%

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Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

Sanz

Perera

Huerta

2012

Applied Soft Computing

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This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.

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“…^signal takes a value of 126.8, where A signal and A" (1) are the root mean square (RMS) amplitude for the signal and the noise, respectively.…”

Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

Sanz

Perera

Huerta

2012

Applied Soft Computing

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“…Lina [13] presented a dynamic model of a plastic gear pair considering tooth wear fault, results show that variation of tooth profiles caused by cumulative sliding wear effect had a significant influence on contact load. Parey [14] developed a six DOF gear dynamic model including localized tooth defect, sinusoidal pulse had been used to simulate the effect of pitting in the gear dynamic model, dynamic responses were solved by differential method, however, effect of faults to the dynamic state of the system were not analyzed.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics Analysis of a Gear-Shaft-Bearing System with Breathing Crack and Tooth Wear Faults

Cui¹

2015

TOMEJ

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Considering backlash, time-varying mesh stiffness and radial clearance of bearing, nonlinear dynamic model of gear bearing flexible shaft system is established taking into account breathing crack in shaft and tooth wear. Nonlinear dynamic equations are solved by Runge-Kutta method. Effect of backlash, crack in shaft and tooth wear faults on the nonlinear dynamic behavior of gear-shaft-bearing system is studied. The results show that gear-shaft-bearing system may change from periodic motion to non-periodic motion as backlash increases, and gear pair change from normal mesh to tooth separation, double-sided impact fault. If crack fault appears, quasi-periodic and chaos motion region increases, and gentle crack fault can result in instantaneous tooth separation and double-sided impact faults. Serious tooth wear fault will also induce tooth separation and double-sided impact faults. If both shaft crack and tooth wear faults exist, tooth wear fault will be intensified by double-sided impact fault from shaft crack, which will result in early failure of the gear system.

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“…With the recent development on EMD method, the technique has already been employed successfully in wide applications : earthquake, climate variability, analysis of daily surface air temperature data, nonlinear ocean waves, detection of structural damage, health-monitoring and so on (Huang and Attoh-Okine, 2005). Recently, literatures of its applications on the failure detection of gear, bearing and rotary machine have been reported (Loutridis, 2004, Parey, et al, 2006, Rai, 2007, and Gao, 2008. Because the vibration signals on gear box is always non-stationary, we try to adopt the EMD method to extract the most suitable feature vectors from the measured signal for fault detection.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis for gear tooth surface damage by empirical mode decomposition in cyclic fatigue test

Fan

Ikejo

Nagamura

et al. 2014

JAMDSM

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Gear is one of the most important and commonly used components in machine system. Some gear failure may lead to fatal damage of the entire system, or even huge losses in industrial production. Early detection of gear damage is crucial to prevent the machine system from malfunction. This paper provides an intelligent diagnosis method for gear damage based on techniques of empirical mode decomposition and support vector machines. By the data processing of empirical mode decomposition, the original signal are decomposed into a finite set of intrinsic mode functions with frequency bands ranging from high to low. The characteristic energy ratios of intrinsic mode functions are acquired as representative parameters of the signal. Furthermore, statistical parameters of standard deviation, root mean square value, kurtosis and skewness are extracted from the original signal. Characteristic energy ratios and statistical parameters are combined as failure feature vectors to be input to the support vector machines classifiers for gear damage diagnosis. The validity of the presented method is confirmed by the application of monitoring gear conditions during the cyclic fatigue test. The vibration accelerations of gear box are acquired to illustrate the progression of pitting damage. Most of the gear conditions are identified, indicating the effectiveness of the proposed method.

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Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect

Cited by 174 publications

References 20 publications

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

Nonlinear Dynamics Analysis of a Gear-Shaft-Bearing System with Breathing Crack and Tooth Wear Faults

Diagnosis for gear tooth surface damage by empirical mode decomposition in cyclic fatigue test

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