Applicability of an Oil Based Calculation Approach for Wear Risk and Wear Lifetime to Grease Lubricated Gear Pairings

Siewerin, Benedikt J.; Dobler, Andreas; Tobie, Thomas; Stahl, Karsten

doi:10.1115/detc2019-97439

Cited by 5 publications

(10 citation statements)

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“…Siewerin et al [20] showed that the grease flow can vary significantly with temperature. Greases with NLGI 1 show more availability on the tooth flanks at a sump temperature of 90 °C compared with 60 °C, thus supporting the circulating lubrication mechanism.…”

Section: Grease Lubrication In Gearboxesmentioning

confidence: 99%

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WITHDRAWN: Numerical Visualization of Grease Flow in a Gearbox

Liu¹,

Dangl²,

Lohner³

et al. 2020

Preprint

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Gearbox lubrication plays a decisive role in both load capacity and efficiency. It is necessary for the formation of a lubricant film in tribological contacts, and for heat dissipation. Grease lubrication is often applied to gearboxes at a low operating speed and means that, for example, less effort is required in sealing design compared to oil lubrication. Over the years, the computational fluid dynamics (CFD) method has frequently been applied to support the lubrication-related design of gearboxes. However, very few studies have focused on grease lubrication. Whereas oil can generally be considered as Newtonian fluid, grease displays complex non-Newtonian flow behavior. In this study, the grease lubrication of a single-stage test gearbox is investigated by means of a finite-volume based CFD model. The numerical results on the grease flow show good accordance with high-speed camera recordings, and clearly display the lubrication mechanisms of “channeling” and “circulating”. These first results on grease flow simulation in gearboxes offer a starting point for further numerical studies.

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Section: Grease Lubrication In Gearboxesmentioning

confidence: 99%

“…section 2). Based on the investigations of Siewerin [20] and Stemplinger [19], the circumferential speeds of vt = {0.05; 0.38} m/s and the grease fill levels of {40; 54; 80} % are considered. The direction of rotation is chosen such that the wheel in Figure 3 rotates clockwise and the pinion counter-clockwise.…”

Section: Operating Conditionsmentioning

confidence: 99%

WITHDRAWN: Numerical Visualization of Grease Flow in a Gearbox

Liu¹,

Dangl²,

Lohner³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The distinct differences in the severity of the wear behavior of experiment A, B, and C fundamentally result from different oil or grease used for lubrication. We will not further investigate this lubrication influence in the present work, but refer to Siewerin et al (2020) for more details.…”

Section: Slow-speed Wear Results -Ground Truthmentioning

confidence: 99%

“…For additional details, refer to Siewerin, Dobler, Tobie, and Stahl (2020) and Schultheiss, Tobie, Michaelis, Höhn, and Stahl (2014), who describe the fundamental experiment setup and procedure.…”

Section: Methodsmentioning

confidence: 99%

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Sendlbeck

Fimpel

Siewerin

et al. 2021

IJPHM

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Gear flank changes caused by wear do not only affect the dynamic behavior of gear systems, but they can also compromise the load-carrying capacity of gear teeth up to critical failure. To help avoid unintended consequences like downtime or safety risks, a condition monitoring system needs to be able to estimate the current wear during operation based on available sensor measurements. While many condition monitoring approaches in research rely on vibrational analysis with manual feature engineering, gearboxes running at slow speed do not reveal much excitation information for this purpose. We therefore introduce an approach for slow-speed gear wear monitoring that is based on the dynamic gear transmission error and that contains an automated feature selection process. For this purpose, we extract a large set of features from the preprocessed transmission error samples. Applying combined filter and embedded feature selection methods enables us to automatically identify and remove features with low relevance. The selection process consists of filtering features with no statistical dependence on the target wear value, removing redundant features with a correlation analysis and a recursive feature elimination process with cross-validation based on a random forest regressor. The remaining relevant set of features is the basis for model training and subsequent wear estimation. For this, the present research employed two independent ensemble models, random forest regression and gradient boosted regression trees. To train and test the proposed approach, we conducted slow-speed gear experiments with developing gear wear on a single-stage spur gear test rig setup. The results of both models show good gear wear estimation performance compared to the actual wear mass loss, even for small quantities. Hence, the proposed transmission error-based approach with automated feature selection is able to quantify the degree of slow-speed wear and offers a possible way for condition monitoring and fault diagnosis.

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“…1. Typical scratches on the addendum and dedendum of a tooth flank due to slow speed wear [6]. and is visible as scratches over the whole tooth flank.…”

Section: Wear In Gear Applicationsmentioning

confidence: 99%

Extended Wear Lifetime Calculation for Grease Lubricated Gears in Consideration of Surface Hardness and Roughness

Siewerin

Tobie²,

Stahl³

2022

MATEC Web Conf.

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Greases have a variety of advantages when special operating conditions apply for gear lubrication, e. g. improved properties against continuous (or slow speed) wear. Often, gears with different surface hardness are combined, in order to reduce costs during manufacturing and heat treatment. However, this has detrimental effect on the wear lifetime. Consequently, the calculation of the wear service life within the gear design process is essential. One common way of wear lifetime prediction is the calculation method acc. to Plewe that is typically combined with a standardized wear test with case carburized gears. It allows to calculate the wear carrying capacity for different gear-lubricant-systems, but a conversion between gear stages of different gear materials is not possible so far. Based on theoretical studies, influences from different surface hardness and surface roughness values as well as lubricating conditions were investigated on FZG back-to-back test rig. The results prove that hard-soft gear pairings have a higher risk for strong wear compared to case hardened gear stages, but basically follow the same trends and mechanisms. Furthermore, surface roughness of the harder gear has an effect on the wear life time of the softer gear. Based on these results the existing calculation method for the wear lifetime of gears was extended.

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Applicability of an Oil Based Calculation Approach for Wear Risk and Wear Lifetime to Grease Lubricated Gear Pairings

Cited by 5 publications

References 0 publications

WITHDRAWN: Numerical Visualization of Grease Flow in a Gearbox

WITHDRAWN: Numerical Visualization of Grease Flow in a Gearbox

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Extended Wear Lifetime Calculation for Grease Lubricated Gears in Consideration of Surface Hardness and Roughness

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