Among the data-driven techniques for abnormalities detection in complex systems, Principal Component Analysis (PCA) is popular because of its simplicity and it does not require prior knowledge. However, the used of PCA is limited to stationary data. This work proposes a methodology to address this limitation. It consists of applying Nuisance Attribute Projection (NAP) in the preprocessing stage before the fault features are transformed with PCA to remove the nonstationarity effects due to the variable operating conditions. The proposal is evaluated to detect inter-turn short-circuits in the stator of a Permanent Magnet Assisted Synchronous Reluctance Motor (PMaSynRM) used in the powertrain of electric vehicles. The variances of the phase currents, computed in moving windows, are used as fault features. The results, obtained with seven fault severities and three load conditions, show that monitoring the Hotelling T2 in the principal subspace leads to good performance, with probabilities of missed detection and false alarms lower than 0.02 and 0.05, respectively. To provide a safety metric, an estimate of the fault level is obtained with an analytical model of the evolution of the slope of the CUmulative SUM decision function with an accuracy greater than 97%.