Resumen
Este estudio demuestra la superioridad de la Transformada Wavelet Discreta frente a la Transformada de Wigner-Ville en la detección de fallas de aislamiento en motores de inducción durante transitorios de arranque. El análisis exhaustivo de 360 señales simuladas reveló que la técnica wavelet con Daubechies 10 alcanza una precisión de clasificación notablemente superior (74.44% vs. 67.78%), superando significativamente a su contraparte. Ciertos niveles de descomposición mostraron sensibilidad máxima con variaciones de hasta +354%, mientras que los indicadores de confiabilidad diagnóstica confirman su robustez. Esta técnica se posiciona como solución óptima para sistemas de monitoreo predictivo, permitiendo la detección temprana de fallas que reduce sustancialmente los tiempos de inactividad y los costos de mantenimiento industrial.Citas
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[7] S. Sayedabbas, R. MohammadHossein, N. Zarift, A. Terheide, and S. Dick, “Condition monitoring and fault detection in small induction motors using machine learning algorithms,” Information, vol. 14, no. 6, p. 329, 2023.
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[12] P. Nussbaumer, M. A. Vogelsberger, and T. M. Wolbank, “Induction machine insulation health state monitoring based on online switching transient exploitation,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1835–1845, Mar. 2015.
[13] I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., vol. 3, pp. 1157–1182, Mar. 2003.
[14] T. E. Sterne and G. D. Smith, “Sifting the evidence—what’s wrong with significance tests?” BMJ, vol. 322, no. 7280, pp. 226–231, Jan. 2001.
[15] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. San Francisco, CA, USA: Morgan Kaufmann, 2011.
[16] J. Bi, K. P. Bennett, M. Embrechts, C. M. Breneman, and M. Song, “Dimensionality reduction via sparse support vector machines,” J. Mach. Learn. Res., vol. 3, pp. 1229–1243, 2003.
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