Interactions between nonlinear spur gear dynamics and surface wear

Ding, Huali; Kahraman, Ahmet

doi:10.1016/j.jsv.2007.06.030

Cited by 134 publications

(81 citation statements)

References 16 publications

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“…Archard's wear model [24,[43][44][45][46][47][48][49] due to its simplicity, however it requires an experimental wear coefficient that is difficult to be determined as it depends on many aspects such as material properties, lubricants, surface quality, operating conditions [24]. In dynamic respect, the wear effect is mainly characterized by loss of tooth profile that represented by modulated mesh excitations [25,50,51], to investigate the effects of surface wear on system's dynamic characteristics.…”

Section: Introductionmentioning

confidence: 99%

Helical gear wear monitoring: Modelling and experimental validation

Brethee

Zhen

et al. 2017

Mechanism and Machine Theory

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Gear tooth surface wear is a common failure mode. It occurs over relatively long periods of service nonetheless, it degrades operating efficiency and lead to other major failures such as excessive tooth removal and catastrophic breakage. To develop accurate wear detection and diagnosis approaches at the early phase of the wear, this paper examines the gear dynamic responses from both experimental and numerical studies with increasing extents of wear on tooth contact surfaces. An experimental test facility comprising of a back-to-back two-stage helical gearbox arrangement was used in a run-tofailure test, in which variable sinusoidal and step increment loads along with variable speeds were applied and gear wear was allowed to progress naturally. A comprehensive dynamic model was also developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication (EHL) principles.The model consists of an 18 degree of freedom (DOF) vibration system, which includes the effects of the supporting bearings, driving motor and loading system. It also couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness and the excitation of different wear severities. Vibration signatures due to tooth wear severity and frictional excitations were acquired for the parameter determination and the validation of the model with the experimental results. The experimental test and numerical model results show clearly correlated behaviour, over different gear sizes and geometries. The spectral peaks at the meshing frequency components along with their sidebands were used to examine the response patterns due to wear. The paper concludes that the mesh vibration amplitudes of the second and third harmonics as well as the sideband components increase considerably with the extent of wear and hence these can be used as effective features for fault detection and diagnosis.

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Section: Introductionmentioning

confidence: 99%

Helical gear wear monitoring: Modelling and experimental validation

Brethee

Zhen

et al. 2017

Mechanism and Machine Theory

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“…The influence of wear on gear dynamic features has already been investigated theoretically [5], but was not validated physically. Therefore, measuring the vibration signal for gear wear assessments is presented in this article.…”

Section: Introductionmentioning

confidence: 99%

Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum

Zhang

et al. 2017

Applied Sciences

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Abstract:As one of the most common gear failure modes, tooth wear can produce nonlinear modulation sidebands in the vibration frequency spectrum. However, limited research has been reported in monitoring the gear wear based on vibration due to the lack of tools which can effectively extract the small sidebands. In order to accurately monitor gear wear progression in a timely fashion, this paper presents a gear wear condition monitoring approach based on vibration signal analysis using the modulation signal bispectrum-based sideband estimator (MSB-SE) method. The vibration signals are collected using a run-to-failure test of gearbox under an accelerated test process. MSB analysis was performed on the vibration signals to extract the sideband information. Using a combination of the peak value of MSB-SE and the coherence of MSB-SE, the overall information of gear transmission system can be obtained. Based on the amplitude of MSB-SE peaks, a dimensionless indicator is proposed to assess the effects of gear tooth wear. The results demonstrated that the proposed indicator can be used to accurately and reliably monitor gear tooth wear and evaluate the wear severity.

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“…Bajpai improved the model proposed by Flodin, taking intentional surface modifications and manufacturing related imperfections into account [5] . Ding studied the two-way relationship between surface wear and gear dynamics [6] . He Rongguo considered the effect of temperature on wear, and gave the calculation formula of the material wear rate [7] .…”

Section: Introductionmentioning

confidence: 99%

A Wear Prediction Model for Spur Gears Based on the Dynamic Meshing Force and Tooth Profile Reconstruction

Zhang¹,

Zheng²,

Wen³

et al. 2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. In this study, Archard's wear equation is combined with a nonlinear dynamic model and a reconstraction method for the wear tooth profile to predict the wear depth of gears. The dynamic model, which is used to determine the dynamic meshing force, and the reconstraction of the wear tooth profile serves as the basis of the sliding coefficient calculation. Next, the dynamic meshing force and sliding coefficient are used to calculate the surface wear in Archard's wear equation. Then, the dynamic meshing force and sliding coefficient would be recalculated according to the surface wear state. After multiple iterations of previous steps, the simulation results show that the non-uniform wear of the gear surface has a great influence on the distribution of dynamic meshing force, and will increase significantly the peak of dynamic meshing force. And in return, the changing dynamic meshing force would enhance the non-uniformity of wear. These two factors influence and exaggerate each other, but are limited by sliding coefficient. When the driving gear runs after 858 million cycles accumulatively, the maximum wear depth reaches 0.0737mm, and the peak value of dynamic meshing force is more than 4 times of the unweared, which exists the risk of overload. The proposed model can be used to predict gears wear life and design gears, which has a certain engineering significance.

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Interactions between nonlinear spur gear dynamics and surface wear

Cited by 134 publications

References 16 publications

Helical gear wear monitoring: Modelling and experimental validation

Helical gear wear monitoring: Modelling and experimental validation

Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum

A Wear Prediction Model for Spur Gears Based on the Dynamic Meshing Force and Tooth Profile Reconstruction

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