Online Fault Detection in Traction PMSM Using a MEMS Accelerometer: A Deep Learning Approach

The modern Automotive sector widely adopts Per- manent Magnet Synchronous Motors (PMSMs) for the propul- sion of Electric Vehicles (EVs). As the market for EVs grows, reducing costs and increasing performance in terms of reliability, range, and efficiency becomes paramount. This work proposes a cost-effective, integrated fault detection system using a MEMS vibration sensor with 3.3 kHz bandwidth and an Autoencoder Neural Network. The model is deployed on the Microcontroller Unit (MCU) that drives the Voltage Source Inverter (VSI), a STM StellarE1 Automotive, utilizing 3 kiB of flash memory (0.97%) and 18 kiB of available RAM (0.6%). This system achieves an accuracy of the 98% in detecting the incipience of a natural bearing fault