Induction motor fault diagnosis using wavelets and coordinate transformations

In this work, we presented a methodology for extraction of patterns for failure classification of three-phase induction machines from their line currents. The signals were acquired from machines under four different conditions: normal operation, stator winding short circuit, rotor broken bars, and bearings faults. The method consists of projecting the signals into the dq axes by use of the Clarke and Park Transforms. The oscillations of these signals, that are due to power quality problems and the faulty conditions, are quantified by the standard deviation of the details components of a wavelet decomposition using several levels. The proposed patterns have visible differences for the machines faulty conditions. Thus, in order to test them, we used a Kohonen Self-Organized Map network to try to classify them obtaining high classification accuracy for machines working with 25% of their nominal torque.