Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Gerardo Avalos-Almazan"'
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:
Gerardo Avalos-Almazan, Sarahi Aguayo-Tapia, Jose de Jesus Rangel-Magdaleno, Mario R. Arrieta-Paternina
Publikováno v:
Machines, Vol 11, Iss 11, p 999 (2023)
This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the applicat
Externí odkaz:
https://doaj.org/article/044eb8e9a0bc4cf0a923b4d2697f1604
Autor:
Sarahi Aguayo-Tapia, Gerardo Avalos-Almazan, Jose de Jesus Rangel-Magdaleno, Juan Manuel Ramirez-Cortes
Publikováno v:
Energies, Vol 16, Iss 12, p 4780 (2023)
Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies su
Externí odkaz:
https://doaj.org/article/2b2a9c3925f44a418f251c7fc2d6ba55
Autor:
Sarahi Aguayo-Tapia, Gerardo Avalos-Almazan, Jose de Jesus Rangel-Magdaleno, Mario R. A. Paternina
Publikováno v:
Entropy, Vol 25, Iss 1, p 44 (2022)
Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in elect
Externí odkaz:
https://doaj.org/article/4c21ad17bc85445ba3a51842758be4a1
Publikováno v:
2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC).