Determining motor´s quality types using principal component analysis on current waveforms

This study proposes a Principal Component Analysis (PCA) method to analyze motor´s current waveforms for determining the motor´s quality types. The proposed method which consists of data training algorithm and motor´s quality types decision algorithm. In the data training algorithm, the input signals are selected from the sample motors with known motor´s quality type. It carries out three major processing stages: (i) the preprocessing stage for enlarging motor´s current waveforms´ amplitude and eliminating noises; (ii) the qualitative features stage for qualitative feature selection of a motor´s current waveform; and (iii) the PCA procedure to obtain the projected space and projected coefficient. This projected space and projected coefficient are then used for motor´s quality type decision. In the motor´s quality type decision algorithm, the input signals are selected from the test motor with unknown quality type. In the experiment, the classified results are 92.80%, 96.83%, 99.91%, and 99.80% for statistical indices Se, PPV, Sp, and NPV. The total classification accuracy was approximately 99.73%.