Zobrazeno 1 - 10
of 1 634
pro vyhledávání: '"intelligent fault diagnosis"'
Publikováno v:
Open Physics, Vol 22, Iss 1, Pp 107815-23 (2024)
Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have re
Externí odkaz:
https://doaj.org/article/f525c842343e435794bd885b81288a5e
Publikováno v:
IEEE Access, Vol 12, Pp 103348-103379 (2024)
This paper presents a comprehensive survey on interpretable intelligent fault diagnosis for rotating machinery, addressing the challenge of the “black box” nature of machine learning techniques that hampers reliability in automated diagnostic pro
Externí odkaz:
https://doaj.org/article/be144f11b4714f3ebac55515b1b02475
Publikováno v:
IEEE Access, Vol 12, Pp 91896-91908 (2024)
An intelligent fault diagnosis method of rolling bearings based on a depth feature fusion network was proposed to solve the problems of a single original signal, large noise interference and difficult diagnosis of varying working conditions of rollin
Externí odkaz:
https://doaj.org/article/5f8437e160e948c5af841065a1965cc5
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 3, Pp 3259-3272 (2024)
Abstract The intelligent fault diagnosis model has made a significant development, whose high-precision results rely on a large amount of labeled data. However, in the actual industrial environment, it is very difficult to obtain a large amount of la
Externí odkaz:
https://doaj.org/article/17acc9dedc3f48719f7d4ebbfb5332b6
Publikováno v:
Machines, Vol 12, Iss 6, p 383 (2024)
Rolling bearings are prone to failure due to the complexity and serious operational environment of rotating equipment. Intelligent fault diagnosis based on convolutional neural networks (CNNs) has become an effective tool to ensure the reliable opera
Externí odkaz:
https://doaj.org/article/5a989932075b4681ae4995407c0590f3
Publikováno v:
Machines, Vol 12, Iss 5, p 305 (2024)
To solve the problem of fault diagnosis for the key components of the CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and an ensemble learning mo
Externí odkaz:
https://doaj.org/article/47bfddab28684f15bcd251b8244de111
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3434 (2024)
Effective real-time health condition monitoring of the roller table and through shaft bearings in the annealing kiln roller system of glass production lines is crucial for maintaining their operational safety and stability for the quality and product
Externí odkaz:
https://doaj.org/article/0f11d2e0ac64486099747178b3555c6d
Autor:
Md Roman Bhuiyan, Jia Uddin
Publikováno v:
Vibration, Vol 6, Iss 1, Pp 218-238 (2023)
In order to evaluate final quality, nondestructive testing techniques for finding bearing flaws have grown in favor. The precision of image processing-based vision-based technology has greatly improved for defect identification, inspection, and class
Externí odkaz:
https://doaj.org/article/0c9ac6d4f279458c8cd0effe214a6ade
Publikováno v:
IEEE Open Journal of the Industrial Electronics Society, Vol 4, Pp 618-628 (2023)
Fault diagnosis is integral to maintenance practices, ensuring optimal machinery functionality. While traditional methods relied on human expertise, intelligent fault diagnosis techniques, propelled by machine learning (ML) advancements, now offer au
Externí odkaz:
https://doaj.org/article/2ce0d41426fe4ac9a231d36b0378b603
Publikováno v:
Sensors, Vol 24, Iss 7, p 2079 (2024)
Deep transfer learning has been widely used to improve the versatility of models. In the problem of cross-domain fault diagnosis in rolling bearings, most models require that the given data have a similar distribution, which limits the diagnostic eff
Externí odkaz:
https://doaj.org/article/bddf40ad974c482ead57e2d2fbc75f9a