Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Motor fault detection"'
Autor:
Skowron Maciej
Publikováno v:
Power Electronics and Drives, Vol 9, Iss 1, Pp 21-33 (2024)
Modern permanent magnet synchronous motor (PMSM) diagnostic systems are now combined with advanced artificial intelligence techniques, such as deep neural networks. However, the design of such systems is mainly focussed on a selected type of damage o
Externí odkaz:
https://doaj.org/article/b5eb0aff79ce439d9bb9a769e3e601dd
Autor:
Chien-Chih Wang
Publikováno v:
Mathematics, Vol 12, Iss 17, p 2652 (2024)
In the chemical industry, stable reactor operation is essential for consistent production. Motor failures can disrupt operations, resulting in economic losses and safety risks. Traditional monitoring methods, based on human experience and simple curr
Externí odkaz:
https://doaj.org/article/ddfc88a69a6b4002b98bf9d9ab133818
Publikováno v:
Sensors, Vol 24, Iss 15, p 5012 (2024)
This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distri
Externí odkaz:
https://doaj.org/article/80adc8fd6fd34a298ca9c578b913dfef
Publikováno v:
Entropy, Vol 26, Iss 4, p 299 (2024)
In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in
Externí odkaz:
https://doaj.org/article/bf34e865ae5e4ee987673f18b839b275
Autor:
Maria Drakaki, Yannis L. Karnavas, Ioannis A. Tziafettas, Vasilis Linardos, Panagiotis Tzionas
Publikováno v:
Journal of Industrial Engineering and Management, Vol 15, Iss 1, Pp 31-57 (2022)
Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction
Externí odkaz:
https://doaj.org/article/9beb2706fac74fdf8d51e64f616bee83
Akademický článek
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Akademický článek
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Publikováno v:
IEEE Access, Vol 7, Pp 139086-139096 (2019)
In most of the fault detection methods, the time domain signals collected from the mechanical equipment usually need to be transformed into frequency domain or other high-level data, highly relying on professional knowledge such as signal processing
Externí odkaz:
https://doaj.org/article/8e2c49678d3b4f08b9b92154ec0d38ce
Autor:
Jose L. Contreras-Hernandez, Dora L. Almanza-Ojeda, Sergio Ledesma, Arturo Garcia-Perez, Rogelio Castro-Sanchez, Miguel A. Gomez-Martinez, Mario A. Ibarra-Manzano
Publikováno v:
Sensors, Vol 22, Iss 7, p 2622 (2022)
Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failur
Externí odkaz:
https://doaj.org/article/0ab33ec10bf5481190e71f6bdd4dce71
Publikováno v:
IEEE Access, Vol 5, Pp 1073-1082 (2017)
Conventional machine stator open-phase fault detection methods rely on the detection of current harmonics from the sensing of phase currents. Because the magnitudes of current harmonics are proportional to the machine load condition, it is a challeng
Externí odkaz:
https://doaj.org/article/9c46cdb9cb2b445ab220f1e30d8d47d2