Abstract:

Electrified high-speed train transportation systems utilize inverter-driven traction motors and a variable-voltage variable-frequency (VVVF) control scheme to achieve high efficiency across a wide range of speeds. However, due to the varying operating conditions, it becomes challenging to monitor the health of traction motors, especially when the train voltage and speed are unknown or not accurately measured. In this paper, we address the problem of extracting current signatures from train traction motors for fault detection without knowing the instant voltage amplitude and the instant motor speed. We propose a robust algorithm that combines minimum-variance spectral analysis to mitigate the effects of varying voltage and a speed compensation technique to tackle the varying-speed issue for extracting fault signatures in the stator current. Experimental results on different current data, including faulty laboratory motor current, onsite measurements from healthy train motors, as well as synthesized faulty train motor current, show that our method can accurately estimate the motor speed and achieve a robust spectrum under various operating conditions for fault signature extraction.