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A model-based signal processing method for fault diagnosis in PMSM machine

In this paper, a model based signal processing method is proposed to diagnose two faults under different load condition for permanent magnet synchronous machines. This method captures the spectrum of stator current in a compact representation. Then, its dimensionality is reduced using the discriminant analysis. Finally a support vector machine automatically classifies the health state of the system. For testing the method, a customized test rig is built and data from different loads and faults are collected. An average accuracy of 93% in a 10-fold cross validation is obtained.