Zobrazeno 1 - 10
of 5 167
pro vyhledávání: '"rolling element"'
Autor:
Wang, Zi
Publikováno v:
Engineering Computations, 2024, Vol. 41, Issue 6, pp. 1507-1528.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/EC-09-2023-0550
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Aiming at the problem that the edge artifacts of Si3N4 ceramic bearing rolling element microcracks have low contrast, contain noise, and easily merge with the background, making it difficult to segment. A method based on 2D discrete wavelet
Externí odkaz:
https://doaj.org/article/d17bad3616f3492a90d30b931a29a86b
Publikováno v:
Dynamics, Vol 4, Iss 2, Pp 303-321 (2024)
The emergence of electric vehicles has brought new issues such as the problem of rolling element bearings (REBs) operating at high speeds. Losses due to these components in mechanical transmissions are a key issue and must therefore be taken into acc
Externí odkaz:
https://doaj.org/article/fa373fa268fe4261bfe0986847558918
Publikováno v:
Proceedings on Engineering Sciences, Vol 6, Pp 281-290 (2024)
The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a
Externí odkaz:
https://doaj.org/article/871a080b9c0d458abc7a41d8afe56a29
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 1, Pp 316-341 (2024)
This study introduces an efficient methodology for addressing fault detection, classification, and severity estimation in rolling element bearings. The methodology is structured into three sequential phases, each dedicated to generating distinct mach
Externí odkaz:
https://doaj.org/article/52e9ad7e34dd4043abb62c9dd556a4af
Autor:
Nistane, Vinod
Publikováno v:
World Journal of Engineering, 2022, Vol. 21, Issue 1, pp. 170-185.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/WJE-06-2022-0244
Ai-enhanced fault diagnosis in rolling element bearings: A comprehensive vibration analysis approach
Publikováno v:
FME Transactions, Vol 52, Iss 3, Pp 450-460 (2024)
This research presents a comprehensive approach for bearing fault diagnosis using artificial intelligence (AI), particularly through the application of artificial neural networks (ANNs). By integrating these networks into vibration analysis, the appr
Externí odkaz:
https://doaj.org/article/dd36169aae1a41ecb6ead8540768989b
Autor:
Florian de Cadier de Veauce, Yann Marchesse, Thomas Touret, Christophe Changenet, Fabrice Ville, Luc Amar, Charlotte Fossier
Publikováno v:
Lubricants, Vol 12, Iss 11, p 362 (2024)
This study investigates the power losses of rolling element bearings (REBs) lubricated using an oil bath. Experimental tests conducted on two different deep-groove ball bearings (DGBBs) provide valuable insights into the behaviour of DGBBs under diff
Externí odkaz:
https://doaj.org/article/8e4e882b9d424b53806696034326a02e
Publikováno v:
Applied Sciences, Vol 14, Iss 15, p 6809 (2024)
The prediction of the health status of critical components is an important influence in making accurate maintenance decisions for rotating equipment. Since vibration signals contain a large amount of fault information, they can more accurately descri
Externí odkaz:
https://doaj.org/article/7ce0329d89fe40f58ba766adaccce0fc
Publikováno v:
Tribology Online, Vol 18, Iss 6, Pp 373-384 (2023)
The development of subsurface microstructural alterations known as dark etching regions (DERs) and white etching bands (WEBs) in rolling element bearings due to rolling contact fatigue have been investigated for the past eight decades, focusing on th
Externí odkaz:
https://doaj.org/article/e19ddd2c90d34e34a3d57f78f53cfec3