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The non detection of bearing faults in rotating machines can lead to less availability, reliability and safety while increasing the maintenance costs due to unexpected breakdowns and urgent repairing. This paper deals with feature engineering to enhance the performance of an early fault detection and diagnostic of ball bearings of asynchronous electrical motors. The features of different types, ie. time, frequency and time-frequency, are extracted from both current and vibration. Then, they are selected based on a genetic algorithm to continuously capture the health state of the ball bearings. The proposed method is applied on sensor signals acquired from a test bench reproducing a real industrial system. The obtained results show the effectiveness of the method particularly for fault detection and diagnostic using current signals which can be useful in practical applications.

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Nowadays, the typical railway traction chain, which operates under alternative power supplies, includes a heavy and bulky transformer. In order to reduce the size and the weight of the input transformer, a medium frequency topology, which involves switches in soft commutation mode, is proposed. A test bench, based on one elementary stage of the complete structure, was built. It allows characterizing new high voltage Silicon Carbide devices in soft switching conditions. Switching losses measurements are performed to determine the optimal switching frequency and the operating limits of the converter.

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