Taxonomy of Induction-Motor Mechanical-Fault Based on Time-Domain Vibration Signals by Multiclass SVM Classifiers

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In the present work, faults in induction motors (IM) have been diagnosed by multiclass support vector machine (SVM) algorithms based on time domain vibration signals. The main focus is to classify mechanical faults of induction motors, i.e. the bearing fault, unbalanced rotor, bowed rotor and rotor misalignment at different rotational speeds and diverse loading conditions. In this work, an induction motor test setup was used to generate vibration signals of seeded mechanical faults. For the effective fault diagnosis, one-versus-one multiclass SVM approach with the Gaussian-radial basis function (RBF) kernel has been used. For the fault classification, firstly optimum statistical features from higher statistical moments have been selected. Also the selection of SVM kernel parameters, numbers of feature datasets and optimum ratio of training-to-testing data have been performed. The SVM classifier is trained and tested at the same rotational speeds as the measured data as well as innovatively tested at intermediate rotational speeds for which measured data was not available. It is observed that classification accuracy gradually increases with the increase of the rotational speed and with the increase of the load on the IM.