Early detection of current faults in a squirrel cage motor using Discrete Wavelet Transform (DWT)

This paper presents a new method for early detection of broken rings and bars in induction motors operating in steady state. The approach uses multispectral analysis based on the Discrete Wavelet Transform (DWT) applied to stator current analysis. A comparison is made between the results obtained using Daubechies wavelets of levels 44 and 45 (db44 and db45). The method focuses on the energetic analysis of the high-level signal decomposition coefficients to detect the presence of left sideband components, which indicate rotor faults. The experimental results demonstrate the effectiveness of the method in distinguishing between healthy and faulty motor conditions, even under light load conditions where conventional techniques often struggle.

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Publication Details

Journal
International Journal of Computers Communications & Control
Published
2025-05-05
DOI
https://doi.org/10.15837/ijccc.2025.3.6919
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Early detection of current faults in a squirrel cage motor using Discrete Wavelet Transform (DWT)

Mario Peña‐Cabrera, Gastón Lefranc, Ismael López-Juárez, Román Osorio-Comparán et al.
International Journal of Computers Communications & Control
Fault Detection and Control Systems
article

Early detection of current faults in a squirrel cage motor using Discrete Wavelet Transform (DWT)

Mario Peña‐Cabrera, Gastón Lefranc, Ismael López-Juárez, Román Osorio-Comparán, Irving García-Martínez, Oscar Molotla
article en

Abstract

This paper presents a new method for early detection of broken rings and bars in induction motors operating in steady state. The approach uses multispectral analysis based on the Discrete Wavelet Transform (DWT) applied to stator current analysis. A comparison is made between the results obtained using Daubechies wavelets of levels 44 and 45 (db44 and db45). The method focuses on the energetic analysis of the high-level signal decomposition coefficients to detect the presence of left sideband components, which indicate rotor faults. The experimental results demonstrate the effectiveness of the method in distinguishing between healthy and faulty motor conditions, even under light load conditions where conventional techniques often struggle.

International Journal of Computers Communications & ControlVol. 20(3)
Pontificia Universidad Católica de Valparaíso (CL), Universidad Nacional Autónoma de México (MX)
Universidad Nacional Autónoma de México
Openalex Percentile: Top 82%
Fault Detection and Control Systems
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