Coupling pairwise support vector machines for fault classification

Different coupling strategies to reconstruct a multi-class classifier from pairwise support vector machine (SVM)-based classifiers are compared with application to fault diagnostics of a cage induction motor. Power spectrum density estimates of circulating currents in parallel branches of the motor are calculated with Welch's method, and SVMs are trained to distinguish a healthy spectrum from faulty spectra and faulty spectra from each other. Majority voting, a mixture matrix and a multi-layer perceptron network are compared in reconstructing the global classification decision. The comparison is done with simulations and the best method is validated with experimental data.