Multiple-fault diagnosis in induction motors through support vector machine classification at variable operating conditions

This work presents a fault diagnosis strategy for induction motors based on multi-class classification through support vector machines (SVM), and the so-called one-against-one method. The proposed approach classifies four different motor conditions (healthy, misalignment, unbalanced rotor and bearing damage) at variable operating conditions (supply frequency and load torque). The proposed SVMs use signatures from the frequency domain characteristics related to each studied fault. These signatures combine information just from the stator condition: radial vibration and stator currents. To acquire training and validation data in steady state, different experiments were performed using a three-phase induction motor. Thirty-five data sets were obtained at different operating regimes of the induction motor for each specific fault (140 conditions including a no-fault scenario) to validate our study. The SVMs with a Gaussian radial basis function (RBF) were proposed as a kernel for the nonlinear classification process. To select the parameter value of the RBF, a bootstrap technique was used. The resulting accuracy for the fault classification process was on the range 84.8–100%25. © 2016, Springer-Verlag Berlin Heidelberg.

Fault diagnosis; Induction motors; Nonlinear classification Computer aided diagnosis; Failure analysis; Frequency domain analysis; Induction motors; Learning systems; Radial basis function networks; Statistical methods; Stators; Support vector machines; Gaussian radial basis functions; Multi-class classification; Multiple fault diagnosis; Nonlinear classification; One-against-one methods; Support vector machine classification; Three phase induction motor; Variable operating condition; Fault detection

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