###### Abstract  
Today, Permanent Magnet Synchronous Motors (PMSMs) are a dominant choice in industry applications. During operation, different possible faults in the system can occur, so early and automated fault detection and severity estimation are required to ensure smooth operation and optimal maintenance planning. In this direction, outlier detection methods are employed in this paper. The motor's current signals are used to extract useful indicators of the fault, along with d-q transform. Statistical indicators in both time and frequency domains are selected to describe fault-related patterns. Based on the extracted features, three outlier detection methods are investigated: the Isolation Forest, the One Class Support Vector Machine, and the Robust Covariance Ellipse. Each method is investigated through different model parameters to evaluate fault detection and severity estimation capabilities. Finally, an ensemble approach is proposed based on decisions and outlier score ensemble. The proposed methodology is verified through different operating conditions in a PMSM test bench. PMSM; fault detection; severity estimation; Isolation Forest; One Class SVM; Robust Covariance Ellipse; ensemble learning Article Outlier Detection for Permanent Magnet Synchronous Motor (PMSM) Fault Detection and Severity Estimation  
Koutrakos, Kout  
interturn short-circuit fault diagnosis is addressed through Recurrent Neural Networks (RNNs). In that work, negative sequence and positive sequence currents are extracted from three-phase current measurements and fed into an Attention-Based RNN along with the PMSM's operating speed. The authors achieve the evaluation of the severity of the fault under various operating conditions. In [12], raw current signals from the PMSM are fed directly to a Convolutional Neural Network (CNN) to avoid the features extraction and signal processing stage. The proposed method achieves the identification of damages in permanent magnets of a PMSM. An extensive overview of PMSM fault diagnosis, including artificial intelligence and machine learning applications, can also be found in [6; 7; 8]. In outlier detection, the goal is to identify and isolate outliers in the data. Outliers can be categorized into Point Outliers and Collective Outliers [13]. In the first category, a single point in a dataset deviates from the rest, while in the second one, there is a collection of points that deviate from the rest of the dataset. For fault detection and severity estimation of a PMSM, the Collective Outlier case is considered, as with the occurrence of a fault, samples that deviate from the normal operation will appear, especially with increasing severity. Three different widely used methods for outlier detection are investigated in this work. The first one is Isolation Forest, proposed by [PERSON] et al. in [14]. The second one is One Class Support Vector Machine (SVM) [15] and the last one is Robust Covariance Ellipse [16]. The three methods are investigated separately and then an ensemble approach is proposed to address the methods' limitations and further PMSM fault diagnosis challenges. Several challenges exist in the integration of AI and machine learning for outlier detection and fault diagnosis of electrical machines, and particularly for PMSM. A key challenge is related to the Signal Processing and Feature Extraction stage [17]. The features to be used by the diagnostic model must reflect as close as possible the existence of faults. In addition, they must be as independent as possible of load and speed operating conditions. For this reason, this paper proposes the utilization of the Park transform and the transformation of currents in the d-q rotational reference system. The presence of a fault in the motor leads to the distortion of d-q current signals in both time and frequency domains, so statistical indicators are used to describe these changes. An additional challenge in diagnosing PMSM faults has to do with the estimation of the severity of the fault.