STM32 condition-monitoring pack uses Cartesiam tools
The new software function pack is intended for those wishing to use an MCU Discovery kit for industrial condition monitoring.
The FP-AI-NANOEDG1 software pack was developed in partnership with machine-learning (ML) expert and ST Authorized Partner Cartesiam. It offers all the necessary drivers, middleware, documentation, and sample code to capture sensor data, integrate, and run Cartesiam’s NanoEdge libraries. The pack allows developers that don’t have specialist AI skills to speedily create and export custom ML libraries for applications using Cartesiam’s NanoEdge AI Studio tool running on a Windows 10 or Ubuntu PC. The function pack provides full prototyping and validation free of charge on STM32 development boards, before deploying on customer hardware where standard Cartesiam fees apply.
This methodology, established with Cartesiam, can use the industrial-grade sensors on-board a Discovery kit, like the STM32L562E-DK, to capture vibration data from monitored equipment both in normal operating modes and under induced abnormal conditions. The software needed for the configuration and acquisition of sensor data comes with the function pack. NanoEdge AI Studio then analyzes the benchmark data and chooses pre-compiled algorithms from over 500 million possible combinations to create optimized libraries for training and inference. The function-pack software provides stubs for the libraries that are simply replaced for embedding in the application. Once deployed, the device learns the normal pattern of the operating mode locally during the initial installation phase as well as during the lifetime of the equipment, as the function pack allows switching between learning and monitoring modes.
The FP-AI-NANOEDG1 function pack is available now. The STM32L562E-DK Discovery kit contains an STM32L562QEI6QU ultra-low-power MCU, an iNEMO 3D accelerometer and 3D gyroscope, as well as two MEMS microphones, a 240×240 colour TFT-LCD module, and on-board STLINK-V3E debugger/programmer.
More information
https://www.st.com/en/embedded-software/fp-ai-nanoedg1.html
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