Incorporating bearing flexibility into a digital twin for supervised machine learning-based ball bearing diagnostics-Reference-Cited by-同舟云学术

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Incorporating bearing flexibility into a digital twin for supervised machine learning-based ball bearing diagnostics

Published:2024-09 Issue: Volume: Page:1-17
ISSN:1539-7734
Container-title:Mechanics Based Design of Structures and Machines
language:en
Short-container-title:Mechanics Based Design of Structures and Machines

Author:

Sow Souleymane¹²,Chiementin Xavier¹,Rasolofondraibe Lanto²,Cousinard Olivier¹

Affiliation:

1. Institute of Thermic, Mechanics, Material (ITheMM), University of Reims Champagne-Ardennes, Reims, France

2. Center of Research for Information and Communication Science and Technology (CReSTIC), University of Reims Champagne-Ardennes, Reims, France

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15397734.2024.2397453

Reference41 articles.

1. A New Approach to Modeling Surface Defects in Bearing Dynamics Simulations

2. Modeling rotorcraft dynamics with finite element multibody procedures

3. A comparative study on the dynamics of high speed spindles with respect to different preload mechanisms

4. Chatter stability of milling with speed-varying dynamics of spindles

5. A Theoretical Model to Predict Vibration Response of Rolling Bearings to Distributed Defects Under Radial Load

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