Zobrazeno 1 - 10
of 2 160
pro vyhledávání: '"Rolling bearings"'
Autor:
Zenghua Chen, Lingjian Zhu, He Lu, Shichao Chen, Fenghua Zhu, Sheng Liu, Yunjun Han, Gang Xiong
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature
Externí odkaz:
https://doaj.org/article/7870c93981e64460889b2c131e52b603
Publikováno v:
Tribology Online, Vol 19, Iss 1, Pp 95-104 (2024)
Indentations in rolling bearings from particle contamination introduce surface damage because they disturb lubrication and produce stress concentrations. In this article, a methodology is proposed where non-failed bearings that have been running in t
Externí odkaz:
https://doaj.org/article/85fa5512043342ee88b41a6ac45c33ca
Publikováno v:
IET Science, Measurement & Technology, Vol 18, Iss 2, Pp 86-102 (2024)
Abstract Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background n
Externí odkaz:
https://doaj.org/article/f5afe5804d6d477390b2d18632d25879
Autor:
Chun‐Yao Lee, Edu Daryl C. Maceren
Publikováno v:
IET Electric Power Applications, Vol 18, Iss 3, Pp 297-311 (2024)
Abstract Intelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, whi
Externí odkaz:
https://doaj.org/article/229ac409d8524390b07469c1ae12a939
Autor:
G. Geetha, P. Geethanjali
Publikováno v:
IEEE Open Journal of the Industrial Electronics Society, Vol 5, Pp 562-574 (2024)
In machine learning, the extraction of features is necessary for intelligent motor fault diagnosis. In industrial applications, it is necessary to identify the optimal number of features to differentiate various types of fault characteristics with le
Externí odkaz:
https://doaj.org/article/eaa8a6a6be16416c90cca2e5737f55b6
Publikováno v:
Electronic Research Archive, Vol 32, Iss 1, Pp 241-262 (2024)
In response to the challenge of noise filtering for the impulsive vibration signals of rolling bearings, this paper presented a novel filtering method based on the improved Morlet wavelet, which has clear physical meaning and is more conducive to par
Externí odkaz:
https://doaj.org/article/7c39a745a91745458ffe82d0245c91b3
Publikováno v:
Mathematics, Vol 12, Iss 22, p 3591 (2024)
There is a complex dynamic interaction between the aero-engine bearing and the rotor, and the resulting time-varying system parameters have an impact on the nonlinear dynamic characteristics of the rolling bearing-flexible rotor system. In this study
Externí odkaz:
https://doaj.org/article/eae22da1bc6b41678bb2662f9e065c74
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 10, p 1857 (2024)
An approach combining the stochastic resonance, the harmonic wavelet packet transforms and the probability density function was proposed to obtain the early fault signal of a rolling bearing. Firstly, an adaptive variable-scale stochastic resonance w
Externí odkaz:
https://doaj.org/article/f7f6fded4e2a4c148ac334c09e907904
Publikováno v:
Actuators, Vol 13, Iss 10, p 401 (2024)
Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults i
Externí odkaz:
https://doaj.org/article/31d3aea4f2284490b942cab85051a471
Publikováno v:
Applied Sciences, Vol 14, Iss 19, p 9039 (2024)
To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrat
Externí odkaz:
https://doaj.org/article/f177de9a169e44f4b07349a35c835289