Rolling Bearing Analysis

Harris, T. A.; Crecelius, W. J.

doi:10.1115/1.3261135

Cited by 572 publications

(473 citation statements)

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“…The accuracy of this assumption is confirmed by various authors [22][23][24], who present equations for the dissipation of energy between a screw and nut in Eq. (1) and in bearing units in Eq.…”

Section: A Sensorless Methods For Compensation Of Thermal Deformationssupporting

confidence: 60%

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

2017

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Original scientific paper The need to improve the accuracy of positioning of a servo-drive was the stimulus for research on a new sensorless method for compensation of thermal deformations of ball screws, enabling predictive compensation of the elongation of such a screw based on historical data. Models have been developed for the predictive compensation of thermal deformations of ball screws in CNC machines, in the form of single-directional multi-layered neural networks with error back-propagation (MLP), radial basis function neural networks (RBF) and Kohonen networks. Neural networks were developed with different structures and learning parameters, and these networks were compared. Models were evaluated in terms of the effectiveness of operation of the networks. The models were tested on real data. Keywords: ball screw; CNC machine tool; neural network; prediction model; thermal compensation Predvidiva kompenzacija toplinskih deformiranja kuglastih vijaka u CNC strojevima primjenom neuronskih mrežaIzvorni znanstveni članak Potreba za povećanjem točnosti pozicioniranja servo-pogona bila je poticaj za istraživanje nove metode bez senzora za kompenzaciju toplinskih deformiranja kuglastih vijaka, koja će omogućiti predvidivu kompenzaciju izduženja takvih vijaka na temelju prikupljenih podataka. Razvijeni su modeli za predvidivu kompenzaciju toplinskih deformiranja kuglastih vijaka u CNC strojevima, u obliku jednosmjernih višeslojnih neuronskih mreža s unatražnim rasprostiranjem greške (MLP), neuronskih mreža s funkcijom radijalne baze (RBF) i Kohonen mreža. Razvijene su neuronske mreže s različitim strukturama i parametrima učenja, i te su se mreže uspoređivale. Modeli su se procjenjivali prema učinkovitosti mreža. Modeli su se ispitivali s realnim podacima.

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Section: A Sensorless Methods For Compensation Of Thermal Deformationssupporting

confidence: 60%

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

2017

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“…Some time domain features include traditional statistical measures, such as RMS, kurtosis, and Crest Factor. Frequency domain features include the power levels of specific bearing defect frequencies compared against known, health baseline thresholds, which can be very useful in diagnosing a fault [16].…”

Section: Figure 42 Impactenergy Overviewmentioning

confidence: 99%

“…A CF with values in the range 2-6 represents normal operations; whereas, a value higher than six represents a defective component. The crest factor is computed by dividing the peak level of the signal average by the standard deviation (RMS) of the signal average, as shown in Equation (16).…”

Section: Root Mean Square Crest Factor and Kurtosismentioning

confidence: 99%

Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

Sheng¹

2012

109

107

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NOTICEThis report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Executive SummaryUtility-scale wind turbines have historically experienced premature component failures, which subsequently increase the cost of energy. The majority of these failures are caused by faults in the drivetrain, led by the main gearbox. To understand the possible causes for gearbox failures and recommend practices for improvement, the National Wind Technology Center (NWTC), at the National Renewable Energy Laboratory (NREL), started a project called the Gearbox Reliability Collaborative (GRC). Condition Monitoring (CM) is one research area under the GRC. It is a method to assess a system's health, which enables proactive maintenance planning, reduces downtime and operations and maintenance costs, and, to some extent, increases safety.To understand the dynamic responses of wind turbine gearboxes under different loading conditions, the GRC tested two identical gearboxes. One was tested on the NWTC's 2.5 MW dynamometer and the other was first tested in the dynamometer, and then field tested in a turbine in a nearby wind plant. In the field, the test gearbox experienced two oil loss events that resulted in damage to its internal bearings and gears. Since the damage was not catastrophic, the test gearbox was removed from the field and retested in the NWTC's dynamometer before it was disassembled. During the dynamometer retest, various condition monitoring systems, e.g., vibration and oil debris, collected data along with testing condition information. The vibrationbased condition monitoring system and the test condition data enabled NREL to launch a Wind Turbine Gearbox Condition Monitoring Round Robin project, as described in this report. The main objective of this project is to evaluate different vibration analysis algorithms used in wind turbine CM and determine whether typical practices are effective. With the involvement of both academic researchers and industrial partners, the Round Robin provides cutting edge research results to industry stakeholders.Under this project, the collected vibration and testing condition data, along with the test gearbox configuration information, were shared with partners who signed memoranda of understand...

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“…Beside, models which can be found in literature allow for approximation of resistance to motion of pair the rolling elements -cage. These methods are based on empirical coefficients connected with lubrication resistance [5,9], A procedure based on graph theory and matrix approach has been proposed in paper [10]. In this case the reliability evaluation and rolling element analysis is developed.…”

Section: Introduction and Assumptionsmentioning

confidence: 99%

“…The first includes mathematical models which are based on numerical simulations of bearings behavior [1][2][3]. The second group includes an empirical and engineering models [4][5][6] which can be used in practical applications, also for diagnostics [7,8]. Difficulty is placed in connection of both above mentioned ways to utilize models working in real time.…”

Section: Introduction and Assumptionsmentioning

confidence: 99%

A Model for the Determination of the Resisting Torque of the Rolling Bearing Cage Motion of Slow-Speed Kinematic Pairs

Szumiński

Kapitaniak

2005

Meccanica

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Abstract.A simplified, plane model for determination of resisting torques of the rolling bearing cage motion of low speed kinematic pairs is presented. The considerations are devoted to an arbitrary character of the point contact of a rolling element-cage pairs lying in a plane which is crossing the rolling elements in their geometrical centre. The model has been developed on the basis of the analysis of motion kinematics of a bearing track and rolling elements. The proposed algorithm allows for continuous tracking the resisting torque of the cage and its components during the kinematic pair motion.

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Rolling Bearing Analysis

Cited by 572 publications

References 0 publications

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks

Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

A Model for the Determination of the Resisting Torque of the Rolling Bearing Cage Motion of Slow-Speed Kinematic Pairs

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