Prediction of faulty behaviour of a converter based on temperature estimation with machine learning algorithm

A method for predicting a faulty behaviour of an electrical converter, the method comprising:

• receiving an operation point indicator (I) of the electrical converter indicative of an actual operation point of the electrical converter, wherein the electrical converter is connected to a rotating electrical machine to drive the rotating electrical machine, and wherein the operation point indicator (I) comprises at least one of:
• a torque of the rotating electrical machine and a rotational speed of the rotating electrical machine;
• receiving a measured device temperature (Td) of a power semiconductor device of the electrical converter indicative of an actual temperature of the power semiconductor device;
• inputting the operation point indicator (I) as input data into a machine learning algorithm trained with historical data comprising operation point indicators and associated device temperatures, wherein the historical data was recorded during normal operation of a power semiconductor device;
• estimating an estimated device temperature (Td*) with the machine learning algorithm, wherein the estimated device temperature (Td*) represents a device temperature during a normal operation;
• and
• predicting the faulty behaviour (F) by comparing the estimated device temperature (Td*) with the measured device temperature (Td).