Abstract
In this paper, an approach to estimate the severity of stator winding short-circuit faults in squirrel-cage induction motors based on the Cubist model is proposed. This is accomplished by scoring the unbalance in the current and voltage waveforms as well as in Park’s Vector, both for current and voltage. The proposed method presents a systematic comparison between models, as well as an analysis regarding hyper-parameter tunning, where the novelty of the presented work is mainly associated with the application of data-based analysis techniques to estimate the stator winding short-circuit severity in three-phase squirrel-cage induction motors. The developed solution may be used for tele-monitoring of the motor condition and to implement advanced predictive maintenance strategies.
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Acknowledgments
This work has been supported by FCT - Fundação para a Ciência e Tecnologia MCTES, UID/CEC/04516/2013 (NOVA LINCS).
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dos Santos, T., Ferreira, F.J.T.E., Pires, J.M., Damásio, C.V. (2017). Severity Estimation of Stator Winding Short-Circuit Faults Using Cubist. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_18
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DOI: https://doi.org/10.1007/978-3-319-65340-2_18
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