Motor fault detection and classification
Detection and classification of motor faults for predictive maintenance.
Implementing a fault detection and classification framework in motors is the first step of a predictive maintenance strategy. Predictive maintenance helps reduce production costs by proactively addressing potential motor issues to reduce downtime, avoiding production losses, prolonging the equipment lifespan and driving decision making thanks to actual data. However the detection of faults and their classification comes with several challenges such as data collection, preprocessing and feature engineering as well as Machine Learning model interoperability and optimization.
In this use case, we show how the STM32 AI Ecosystem can help you along this journey, and how AI can provide in-depth and automated signal analysis, giving the maintenance team unparalleled reliability and confidence in managing upcoming events.
In this use case, we show how the STM32 AI Ecosystem can help you along this journey, and how AI can provide in-depth and automated signal analysis, giving the maintenance team unparalleled reliability and confidence in managing upcoming events.
Approach
By using the ISE OneX test bench setup, we accurately created and recreated multiple kinds of faults including motor rotation unbalance, loose or faulty bearings, and shaft misalignment.
The goal was to use NanoEdge AI Studio to generate a machine learning library able to classify these faults. Here is how we proceeded:
You can also follow the same steps to create a similar AI model able to classify multiple levels of misalignment (0.0, 0.2, 0.4 and 0.6 mm).
The goal was to use NanoEdge AI Studio to generate a machine learning library able to classify these faults. Here is how we proceeded:
- First, we set up the datalogger generator in NanoEdge AI Studio to continuously record motor vibration data using the STEVAL-PROTEUS1 with its built-in always-on ISM330DHCX sensor.
- We used the recorded vibration data and the Sampling finder tool provided by NanoEdge AI Studio to determine the best combination of signal length and data rate to use for the project.
- During the benchmark process, NanoEdge AI Studio identified several libraries capable of classifying the nominal state of the motor and the 4 kinds of faults with a high accuracy.
- With the validation step in NanoEdge AI Studio, we selected the library that produced the best results on new datasets generated from our tests.
- After compiling the library, we deployed it in the STEVAL-PROTEUS board by Bluetooth using the ST BLE Sensor mobile app.
You can also follow the same steps to create a similar AI model able to classify multiple levels of misalignment (0.0, 0.2, 0.4 and 0.6 mm).
Sensor
3-axis accelerometer featured on the STEVAL-PROTEUS1 wireless smart sensor evaluation board (reference: ISM330DHCX).
Data
5 classes for N-class classification
Signal length 1536 (512 per axis, 3 axis), approximately 1500 signals per class
Data rate 1667 Hz, full scale: 8g
- No fault
- Rotation unbalance
- Loose bearings
- Faulty bearings
- Misalignment
Signal length 1536 (512 per axis, 3 axis), approximately 1500 signals per class
Data rate 1667 Hz, full scale: 8g
Results
Fault classification:
99.80% accuracy, 12.7 Kbytes of RAM, 25.4 Kbytes of Flash memory
99.80% accuracy, 12.7 Kbytes of RAM, 25.4 Kbytes of Flash memory
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.
Compatible with Any STM32 MCU
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.
