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Anomaly detection in an electric motor

Current sensing to detect abnormal behaviors in motors.

Anomaly detection in an electric motor Anomaly detection in an electric motor Anomaly detection in an electric motor
Anomaly detection in an electric motor Anomaly detection in an electric motor Anomaly detection in an electric motor

Industrial

Appliances

NanoEdge AI Studio

Predictive maintenance

Current sensor

Motors 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 strategy by automatically detecting aging or predicting anomalies. Machine learning help making the data generated by the system into meaningful data for Humans. 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. 

Approach

By measuring the current consumption instead of the vibration emitted by the motor, we only need the X-NUCLEO-IHM16M1 board, no additional sensor 
Each phase of the GBM2804H-100T three-Phase Motor contains the same information. For smaller signals we measured only one of them 
We created a dataset of 500 signals for both normal and abnormal behaviors. We changed the speed of the motor to simulate abnormal behaviors 
We created an Anomaly Detection dynamic model in NanoEdge AI studio
We trained it directly on the edge on the P-NUCLEO-IHM03 kit (NUCLEO-G431RB + X-NUCLEO-IHM16M1 + GBM2804H-100T motor) and tested it live 

Sensor

Current senor: X-NUCLEO-IHM16M1 (STM32Nucleo expansion board for current sensing)

Data

Regular and Abnormal signals
- Regular signals: Normal functioning
- Abnormal signals: Different speeds 
Signals length 512 (1 axis)
Data rate 24 kHz

Results

Anomaly detection:
100 % accuracy, 0.6 KB RAM, 2.8 KB Flash
Blue points correspond to normal signals, red points to abnormal ones. 
The signal numbers are on the abscissa and the confidence of the prediction is on the ordinate 

Model created with

NanoEdge AI Studio

Model created with

Compatible with

STM32 series

Compatible with

Resources

Model created with NanoEdge AI Studio

A free AutoML software for adding AI to embedded projects, guiding users step by step to easily find the optimal AI model for their requirements.

Model created with NanoEdge AI Studio

Compatible with STM32 series

The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.

Compatible with STM32 series

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