Electric drive diagnosis (11 faults)

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Electric drives are used for various applications and are becoming increasingly performant. They can be monitored in a very precise way thanks to the data they provide during operation. This data can also be used to enhance the application using Predictive Maintenance techniques.

Predictive maintenance consists in optimizing maintenance strategies by automatically detecting aging or predicting anomalies. Machine learning translates the data generated by the system into meaningful data. We have added AI solution directly next to the Motor Control algorithm to run both anomaly detection & classification and motor control on the same microcontroller, reducing cost of system and optimizing resources. This approach an easily be adapted to many motors and for various applications 


  • The basic components are the drive motor to be tested (permanent magnet synchronous motor), a torque measuring shaft, the test modules and a load motor (synchronous servomotor). 
  • The tests are performed at various bearing loads, torque loads and speeds. 
  • The different combinations of defects, loads and speeds result in 11 classes. 
  • The signal is current based. 


Generic current sensor


>> Download the dataset

11 classes of data: 11 different combinations of defects, loads and speeds
Signal length: 48 (1 axis), 5300 signals per class


11 classes classification:
98.56% Balanced accuracy, 0.5 KB RAM, 140.6 KB Flash

Green points represent well classified signals. Red points represent misclassified signals. The classes are on the abscissa and the confidence of the prediction is on the ordinate