We discuss the unique machine learning core (MLC) capabilities on our Inertial Measurement Unit (IMU), and the path from condition monitoring to predictive maintenance through machine learning.
You’ll need an STWIN SensorTile wireless industrial node (STEVAL-STWINKT1B) with system board embedding a range of industrial-grade sensors and ultralow power Arm Cortex-M4 microcontroller for vibration analysis of 9-DoF motion sensing data across a wide range of vibration frequencies. This includes very high frequency audio, ultrasound spectra, and high precision local temperature and environmental monitoring.
Moreover, the STEVAL-STWINKT1 supports Bluetooth Low Energy wireless connectivity through an on-board module. Wired connectivity is also supported via an on-board RS-485 transceiver and USB.
What you'll learn
- How to use the latest STM32Cube FP-SNS-DATALOG1 function pack to collect, store, and visualize sensor data
- How to connect STWIN via USB, configure sensors, and acquire and tag data
- How to configure STWIN sensors via Bluetooth Low Energy, acquire and store data on a local SD card
- How to use Python scripts to plot and preprocess data for further analysis
- How to configure the machine learning core in the IMU on the STWIN kit with Unico-GUI to perform an example classification, independently from the main MCU
Ready to get started?
The videos are available in our YouTube playlist embedded below, or at the following URL:
https://www.youtube.com/playlist?list=PLnMKNibPkDnFDuZH6WHXRmGNgJS4rFZSk
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