In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three “problem transformation” methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time–frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time–frequency transformation.
Georgoulas, G., Climente-Alarcon, V., Antonino-Daviu, J. A., Tsoumas, I. P., Stylios, C. D., Arkkio, A., & Nikolakopoulos, G. (2016). The Use of a Multilabel Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults Based on the Start-Up Transient. IEEE Transactions on Industrial Informatics, 13(2), 625-634. https://doi.org/10.1109/TII.2016.2637169