A Belief Function Solution for Stator Insulation Robustness Study

This paper proposes a model-based decision taking solution for electrical machines winding insulation robustness study. The solution is based on the Belief Function (BF) theory. It is processed in two main steps: a first one aims to learn Weibull model parameters from some labeled aging Partial Discharge Inception Voltage (PDIV) data. Then a second classification step separates some unlabeled PDIV data according to the learnt Weibull models. The classification results can give information on the robustness and the reliability of the Electrical Insulation System (EIS) under a thermal constraint.