Classification of inter-turn short-circuit faults in induction motors based on quaternion analysis

Short-circuit in three-phase engines are highly destructive faults, which overheat and damage internal elements reducing efficiency and lifetime. New multi-class approaches are best trained with measurements from three-phase motors instrumented with short-circuit faults because it offers natural and physical signal behavior. This work overcomes the lack of datasets by acquiring current signals from an instrumented induction motor to create a dataset of inter-turn short-circuit (ITSC) faults at four levels per phase. The dataset generated consists of 13 categories with five repetitions per trial for a squirrel cage motor induction. The proposed classification method is based on quaternions that simultaneously model the three-phase signals as pure quaternions. Three statistical features are extracted from quaternions, and a decision tree classifier is trained per feature. Thereby, a boosting scheme is used to calculate the resulting category. Boosting method improves the classification results of decision tree models, showing fast, accurate, and robust performance.