Zobrazeno 1 - 10
of 963
pro vyhledávání: '"incipient fault"'
Autor:
Abdul Haleem Medattil Ibrahim, Sajan K. Sadanandan, Tareg Ghaoud, Vetrivel Subramaniam Rajkumar, Madhu Sharma
Publikováno v:
IEEE Access, Vol 12, Pp 112822-112838 (2024)
This review paper explores the landscape of incipient fault detection methodologies within power distribution networks. It aims to provide insights into the current state-of-the-art techniques, their effectiveness, and potential avenues for future re
Externí odkaz:
https://doaj.org/article/e784881001834360b12b244ea55fbbd7
Publikováno v:
IEEE Open Access Journal of Power and Energy, Vol 11, Pp 532-545 (2024)
Incipient faults (IFs) are abnormal states before the permanent failure of power equipment. IFs are typically transient and generally do not trigger the operation of relay protection devices. This leads the difficulty in capturing IF data from wavefo
Externí odkaz:
https://doaj.org/article/70389b81df6648f4b3ef8f2eed2bb07a
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 9958 (2024)
Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even
Externí odkaz:
https://doaj.org/article/0d2f2d3d0cf541f294d41286d6ce99dc
Autor:
Feng Wang, Pengping Zhang, Jianxiu Li, Zhiqi Li, Mingzhe Zhao, Yongliang Liang, Guoqiang Su, Xinhong You
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
In the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not ad
Externí odkaz:
https://doaj.org/article/371a640267db415d876d9ca42a574f33
Autor:
Carlos Javier Morales-Perez, Laritza Perez-Enriquez, Juan Pablo Amezquita-Sanchez, Jose de Jesus Rangel-Magdaleno, Martin Valtierra-Rodriguez, David Granados-Lieberman
Publikováno v:
Machines, Vol 12, Iss 6, p 399 (2024)
Induction motors are one of the most used machines because they provide the necessary traction force for many industrial applications. Their easy operation, installation, maintenance, and reliability make them preferred over other electrical motors.
Externí odkaz:
https://doaj.org/article/1bcbb855e8154fb5adc3f0e7588cd2d4
Publikováno v:
Journal of Chemical Engineering of Japan, Vol 56, Iss 1 (2023)
Support vector data description (SVDD) is a classical process monitoring skill and usually uses Euclidean distance to evaluate the status of a process. It should be noted that the proposed evaluation method restricts the detection performance for som
Externí odkaz:
https://doaj.org/article/d56a22ed7d9b48e081ba96bf136bdb53
Autor:
Li Zhaofei
Publikováno v:
Jixie chuandong, Vol 47, Pp 165-176 (2023)
On-line real time diagnosis of bearing incipient faults is the intersection of practical engineering application requirements and basic scientific research. It is one of the development directions of bearing fault diagnosis at home and abroad. Firstl
Externí odkaz:
https://doaj.org/article/afac844ef77e494991b1e81267150939
Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching
Autor:
Aliyu Abubakar, Christos Zachariades
Publikováno v:
Sensors, Vol 24, Iss 11, p 3565 (2024)
This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while
Externí odkaz:
https://doaj.org/article/16327318e763417eaee17b5a0659dd3e
Publikováno v:
IEEE Access, Vol 11, Pp 58822-58834 (2023)
To effectively recognize the incipient potential faults caused by degradation of multiple components in DC-DC converters, a fault diagnosis method that involves multi-dimensional feature fusion and sensitive feature extraction is proposed. Firstly, t
Externí odkaz:
https://doaj.org/article/81b16cf8d23748649a9932dc55b15b13
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