Methodology for the physics-based modelling of multiple rolling element bearing configurations

Leturiondo, Urko; Salgado, Óscar; Galar, Diego

doi:10.1177/1464419316660930

Cited by 5 publications

(4 citation statements)

References 58 publications

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“…Nevertheless, these models are very limited and not all physical phenomena regarding the dynamic behavior of bearings are considered. Hence, for a more detailed investigation, bearing dynamics such as nonlinear spring behavior, variable bearing stiffness, time-dependent excitation of ball bearings, dynamic loadings and so forth are beneficial [ 30 , 31 , 32 , 33 ]. These models are based on second order differential equations with a time integration method and differ mainly in the number of DOF (degree of freedom) and the calculus of contacts between the bearing parts (inner ring, rolling elements, outer ring, shaft, etc.).…”

Section: Synthetic Data For Trainingmentioning

confidence: 99%

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Kahr

Kovács

Loinig³

et al. 2022

Sensors

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Rolling element bearing faults significantly contribute to overall machine failures, which demand different strategies for condition monitoring and failure detection. Recent advancements in machine learning even further expedite the quest to improve accuracy in fault detection for economic purposes by minimizing scheduled maintenance. Challenging tasks, such as the gathering of high quality data to explicitly train an algorithm, still persist and are limited in terms of the availability of historical data. In addition, failure data from measurements are typically valid only for the particular machinery components and their settings. In this study, 3D multi-body simulations of a roller bearing with different faults have been conducted to create a variety of synthetic training data for a deep learning convolutional neural network (CNN) and, hence, to address these challenges. The vibration data from the simulation are superimposed with noise collected from the measurement of a healthy bearing and are subsequently converted into a 2D image via wavelet transformation before being fed into the CNN for training. Measurements of damaged bearings are used to validate the algorithm’s performance.

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Section: Synthetic Data For Trainingmentioning

confidence: 99%

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Kahr

Kovács

Loinig³

et al. 2022

Sensors

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“…The excitation of the spindle system is the nonlinear bearing force and the eccentric force of the spindle under the high-speed rotation. The dynamic differential equation of the simplified spindle system is shown in equation (22). where m p is the equivalent mass of the rotor on the spindle.…”

Section: Dynamic Modeling Of Bearing Rotor Systemmentioning

confidence: 99%

“…Yang et al 21 calculated the bearing stiffness under different combinations form and analyzed the variation law by establishing the stiffness matrix of the bearing. Leturiondo et al 22 studied the effect of bearing size as well as configuration on its dynamic characteristics by developing a bearing mathematical dynamic model. Yan et al 23 proposed a new modeling method for spindle thermal errors based on the uniform and non-uniform variation patterns of the temperature field of motorized spindles.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear dynamic analysis of ceramic motorized spindle considering bearing preload and span

Zhang

Liuwang

Zhan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Ceramic motorized spindle is more suitable for high-precision and high-speed machining because of its material with high stiffness, low deformation, and other excellent characteristics. The ceramic bearing assembly and support location are critical elements influencing the stiffness and dynamic characteristics of ceramic motorized spindle. In order to investigate the influence of bearing assembly on the dynamic characteristics of spindle, the relationship between preload and bearing stiffness is determined and a dynamic model of ceramic motorized spindle bearing rotor system is established taking the comprehensive impact of bearing span and preload into account. The bearing stiffness fluctuation caused by bearing preload and bearing thermal deformation is calculated, and the effect of bearing preload and span on the dynamic characteristics is investigated. The results show that the rotor amplitude firstly decreases with the bearing preload and span vary, and then increases. The best assembly combination of ceramic spindle is a preload of 400 N and a span of 190 mm in the speed of 12,000–18,000 r/min. The vibration experiments are conducted on the ceramic motorized spindle, and the average error between experimental date and calculated date is 2.49%, which verifies the accuracy of the model. This study lays a theoretical foundation for further investigation of the dynamic characteristics of ceramic motorized spindle and its structural optimization.

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“…The test rig model draws on a detailed model for REBs developed by Leturiondo et al 24 These authors presented a methodology to create a physics-based model to reproduce the dynamics of multiple kinds of REBs. Following a multi-body modelling approach, the proposed strategy takes advantage of the reusability of models to cover a wide range of REB configurations, and to generalise the dimensioning of the REB and the application of the operating conditions.…”

Section: Mechanical Modelmentioning

confidence: 99%

Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis

Leturiondo

Salgado

Galar

2016

Structural Health Monitoring

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Diagnosis and prognosis are key issues in the application of condition based maintenance. Thus, there is a need to evaluate the condition of a machine. Physics-based models are of great interest as they give the response of a modelled system in different operating conditions. This strategy allows for the generation of synthetic data that can be used in combination with real data acquired by sensors to improve maintenance. The article presents an electromechanical model for a rotating machine, with special emphasis on the modelling of rolling element bearings. The proposed model is validated by comparing the simulation results and the experimental results in different operating conditions and different damaged states. This comparison shows good agreement, obtaining differences of up to 10% for the modelling of the whole rotating machine and less than 0.6% for the model of the bearing.

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Methodology for the physics-based modelling of multiple rolling element bearing configurations

Cited by 5 publications

References 58 publications

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Nonlinear dynamic analysis of ceramic motorized spindle considering bearing preload and span

Validation of a physics-based model of a rotating machine for synthetic data generation in hybrid diagnosis

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