Authors: José D MartínezMorales Elvia R PalaciosHernández D U CamposDelgado
Publish Date: 2016/11/21
Volume: 100, Issue: 1, Pages: 59-73
Abstract
This work presents a fault diagnosis strategy for induction motors based on multiclass classification through support vector machines SVM and the socalled oneagainstone 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 threephase induction motor Thirtyfive data sets were obtained at different operating regimes of the induction motor for each specific fault 140 conditions including a nofault 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 848–100
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