RT info:eu-repo/semantics/article
T1 Time-frequency analysis based on minimum-norm spectral estimation to detect induction motor faults
A1 García Calva, Tomás Alberto
A1 Moríñigo Sotelo, Daniel
A1 Duque Pérez, Óscar
A1 Garcia Perez, Arturo
A1 Romero Troncoso, René de Jesús
K1 Control engineering
K1 Electric motors, Induction
K1 Motores eléctricos
K1 Motores de induccion
K1 Signal processing
K1 Speech processing systems
K1 Spectrum analysis - Statistical methods
K1 Time-series analysis
K1 Frequency spectra
K1 Fault detection
K1 3306 Ingeniería y Tecnología Eléctricas
AB In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.
PB MDPI
SN 1996-1073
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/58935
UL https://uvadoc.uva.es/handle/10324/58935
LA eng
NO Energies,  2020, Vol. 13, Nº. 16, 4102
NO Producción Científica
DS UVaDOC
RD 11-may-2024