All the equipment interacts with their environment by emitting various signals. These signals are source of relevant information reflecting equipment functioning. Being able to understand these signals allows significant optimization capabilities.
For example, before an anomaly or failure occurs, your machine generates slightly abnormal vibration pattern. By placing a sensor on the machine, we can monitor its activity. Thanks to Machine Learning, we learn directly from the machine what is its normal functioning. By analyzing the evolution of vibrations, we can detect the appearance of an anomaly. We have implemented this approach on a fan motor for demonstration purposes, but this approach can easily be adapted to many industrial machines.
Approach
- We put the accelerometer / board on a fan. We stick it using blu-tack
- 300 regular signals: 3 speeds (low, medium, high), 100 signals per speed
- 300 abnormal signals: for each speed, block the air flow, move the fan, tap on it
- We created an ‘Anomaly detection’ project on NanoEdge AI Studio and tested it live on a NUCLEO-L432KC
Sensor
Accelerometer (3-axis): LIS3DH
Data
Regular and Abnormal signals:
– Regular signals: 100 signals per speed (low, medium, high)
– Abnormal signals: for each speed, block the air flow, move the fan, tap on it, etc.
Signal length: 1536 (512 per axis, 3 axes)
Data rate: 1.6 kHz, Range: 2g
Results
Anomaly detection classes:
100 % accuracy, 7.8 KB RAM, 6.1 KB Flash
The signal numbers are on the abscissa and the confidence of the prediction is on the ordinate
