Automatic Classification of Field Winding Faults in Synchronous Motors based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals

[EN] In this work, the application of the bicoherence (a squared normalized version of the bispectrum) of the stray flux signal is proposed as a way of detecting faults in the field winding of synchronous motors. These signals are analyzed both under the starting and at steady state regime. Likewise, two quantitative indicators are proposed, the first one based on the maximum values of the asymmetry and the kurtosis of the bicoherence matrix obtained from the flux signals and the second one relying on an algorithm based on the bicoherence image segmentation of the obtained pattern for each analyzed state. The results are analyzed through a comparative study for the two considered motor regimes, obtaining satisfactory results that sustain the potential application of the proposed methodology for the automatic field winding fault detection in real applications.Miguel E. Iglesias Martínez s work was supported by the postdoctoral research scholarship "Ayudas para la recualificación del sistema universitario español 2021-2023. Modalidad: Margarita Salas", UPV, Ministerio de Universidades, Plan de Recuperación, Transformación y Resiliencia, Spain. Funded by the European Union-Next Generation EU. This work is also supported by Generalitat Valenciana (reference CIAICO/2021/020)Iglesias-Martínez, ME.; Guerra Carmenate, J.; Antonino-Daviu, J.; Dunai, L.; Platero, CA.; Conejero, JA.; Fernández De Córdoba, P. (2023). Automatic Classification of Field Winding Faults in Synchronous Motors based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals. IEEE Transactions on Industry Applications. 59(4):3945-3954. https://doi.org/10.1109/TIA.2023.32622203945395459