Digital Twin-University of Ottawa Induction Motor Synthetic Dataset (DT- UOIMSD)

The purpose of this dataset is to evaluate whether synthetically generated motor fault data can be used to train machine learning models that successfully transfer to real laboratory measurements, without requiring real data during training.

The digital twin replicates the vibration signatures of eight induction motor health conditions healthy, rotor unbalance, rotor misalignment, stator winding fault, voltage unbalance, bowed rotor, broken rotor bars, and faulty bearings under four constant speed conditions (15 Hz, 30 Hz, 45 Hz, and 60 Hz) at both unloaded and loaded states. Synthetic signals were calibrated against the real UOEMD-VAFCVS measurements using spectral analysis, with signal amplitudes and dominant frequency content matched to the real dataset.