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The insulated gate bipolar transistor (IGBT) is a power transistor that is used in medium to high power, and low frequency applications. These applications include railway traction motors, wind turbines, electric and hybrid vehicles and uninterrupted power supplies. IGBT failures can result in reduced efficiency of the system or lead to system failure. Anomaly detection techniques can provide early warning of IGBT failures leading to cost benefits by avoidance of unscheduled maintenance and improved safety. One approach to detect anomalies in IGBTs is to monitor the collector-emitter current and voltage in application. These current and voltage parameters can then be used to compute a distance measure called the Mahalanobis Distance (MD). The MD values with the use of an appropriate threshold enable anomaly detection of these devices. The computation of the MD measure requires calculating the covariance between the parameters monitored. The presence of outliers in the monitored data can lead to the overestimation of the covariance matrix that in turn affects the anomaly detection results. This issue can be addressed by the use of robust covariance estimation techniques. In this study, three different robust covariance estimators are evaluated to determine the impact of the use of these techniques on the anomaly detection time obtained by MD.