RT info:eu-repo/semantics/article
T1 Early detection of faults in induction motors—A review
A1 García Calva, Tomás Alberto
A1 Moríñigo Sotelo, Daniel
A1 Fernández Cavero, Vanessa
A1 Romero Troncoso, René de Jesús
K1 Artificial intelligence
K1 Electric motors, Induction
K1 Motores de inducción
K1 Fault diagnosis
K1 Fault location (Engineering)
K1 Machine learning
K1 Aprendizaje automático
K1 Quality control
K1 Control de calidad
K1 Signal processing
K1 Procesamiento de señales
K1 1203.04 Inteligencia Artificial
K1 3306.03 Motores Eléctricos
K1 3306 Ingeniería y Tecnología Eléctricas
AB There is an increasing interest in improving energy efficiency and reducing operational costs of induction motors in the industry. These costs can be significantly reduced, and the efficiency of the motor can be improved if the condition of the machine is monitored regularly and if monitoring techniques are able to detect failures at an incipient stage. An early fault detection makes the elimination of costly standstills, unscheduled downtime, unplanned breakdowns, and industrial injuries possible. Furthermore, maintaining a proper motor operation by reducing incipient failures can reduce motor losses and extend its operating life. There are many review papers in which analyses of fault detection techniques in induction motors can be found. However, all these reviewed techniques can detect failures only at developed or advanced stages. To our knowledge, no review exists that assesses works able to detect failures at incipient stages. This paper presents a review of techniques and methodologies that can detect faults at early stages. The review presents an analysis of the existing techniques focusing on the following principal motor components: stator, rotor, and rolling bearings. For steady-state and transient operating modes of the motor, the methodologies are discussed and recommendations for future research in this area are also presented.
PB MDPI
SN 1996-1073
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61647
UL https://uvadoc.uva.es/handle/10324/61647
LA eng
NO Energies, 2022, Vol. 15, Nº. 21, 7855
NO Producción Científica
DS UVaDOC
RD 18-nov-2024