Abstract
This study demonstrates the superiority of the Discrete Wavelet Transform over the Wigner-Ville Transform in detecting insulation faults in induction motors during startup transients. The comprehensive analysis of 360 simulated signals revealed that the wavelet technique with Daubechies 10 achieves significantly higher classification accuracy (74.44% vs. 67.78%), substantially outperforming its counterpart. Certain decomposition levels showed maximum sensitivity with variations up to +354%, while diagnostic reliability indicators confirm its robustness. This technique positions itself as an optimal solution for predictive monitoring systems, enabling early fault detection that substantially reduces downtime and industrial maintenance costs.
References
[2] P. K. Sahoo and A. S. Hati, “Review on machine learning algorithm based fault detection in induction motors,” Arch. Comput. Methods Eng., vol. 28, no. 3, pp. 1929–1940, 2020.
[3] K. N. Gyftakis and A. J. Marques Cardoso, “Reliable detection of stator interturn faults of very low severity level in induction motors,” IEEE Trans. Ind. Electron., vol. 68, no. 4, pp. 3475–3484, Apr. 2021.
[4] R. M. Tallam, S. B. Lee, G. C. Stone, G. B. Kliman, T. G. Habetler, and J. Yoo, “A survey of methods for detection of stator-related faults in induction machines,” IEEE Trans. Ind. Appl., vol. 43, no. 4, pp. 920–933, Jul./Aug. 2007.
[5] S. Grubic, J. M. Aller, B. Lu, and T. G. Habetler, “A survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems,” IEEE Trans. Ind. Electron., vol. 55, no. 12, pp. 4127–4136, Dec. 2008.
[6] S. Sobhi, M. H. Reshadi, 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.
[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.
[8] M. Misiti, Y. Misiti, G. Oppenheim, and J.-M. Poggi, Wavelet Toolbox™ User’s Guide. Natick, MA, USA: MathWorks, 2021.
[9] I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA, USA: SIAM, 1992.
[10] Y. Y. Tang, Wavelet Theory and Its Application to Pattern Recognition. Singapore: World Scientific, 2000.
[11] L. Cohen, Time-Frequency Analysis. Englewood Cliffs, NJ, USA: Prentice-Hall, 1995.
[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.
This work is licensed under a Creative Commons Attribution 4.0 International License.