Comparison of the effectiveness between the Wavelet Transform and the Wigner-Ville Transform for the diagnosis of low insulation in the starting transient of induction motors

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.