Condition monitoring is a major component of predictive maintenance systems, allowing production performance improvement, cost reduction and a drastic decrease of the downtime due to routine maintenance.
The FP-AI-NANOEDG1 function pack helps to jump-start the implementation and development of condition monitoring applications designed with the NanoEdge™ AI Studio solution from Cartesiam (a member of the ST Partner program).
NanoEdge™ AI Studio simplifies the creation of autonomous Machine Learning libraries with the possibility of running not just inference but also training on the edge. It facilitates the integration of predictive maintenance capabilities as well as the security and detection with sensor patterns self-learning and self-understanding, exempting users from special skills in mathematics, Machine Learning, data science, or creation and training of Neural Network.
FP-AI-NANOEDG1 covers the entire design of the Machine Learning cycle from the data set acquisition to the integration of NanoEdge™ AI Studio generated libraries on a physical node. It runs the inference in real time on an STM32L562QE ultra-low-power microcontroller (Arm® Cortex®-M33 at 110 MHz with 512 Kbytes of Flash memory and 256 Kbytes of SRAM), taking physical sensor data as input. The NanoEdge™ library generation itself is out of the scope of this function pack and must be generated using NanoEdge™ AI Studio.
FP-AI-NANOEDG1 implements a wired interactive command-line interface (CLI) to configure the node, record data, and manage learning and detection phases. However, all these operations can also be performed in a standalone battery-operated mode through the user button, without having the console. A simple UI implemented on the LCD monitors the processing and its outcome.
- Complete firmware to program an STM32L5 sensor node for condition monitoring and predictive maintenance applications
- Stub for replacement with a Cartesiam Machine Learning library generated using the NanoEdge™ AI Studio for the desired AI application
- Configuration and acquisition of STMicroelectronics iNEMO LSM6DSO 3D digital accelerometer and gyroscope
- Data logging on a microSD™ card
- Simple LCD user interface
- Autonomous mode controlled by user button
- Interactive command-line interface (CLI) for
- Node and sensor configuration
- Data logging
- Learning and detection phase management of the NanoEdge™ library
- Easy portability across STM32 microcontrollers by means of the STM32Cube ecosystem
- Free and user-friendly license terms
|Part Number||General Description||Software Version||Supplier||GitHub link||Download||Previous versions|
|FP-AI-NANOEDG1||Artificial Intelligence (AI) condition monitoring function pack for STM32Cube||1.0.1||ST||-|
MadeForSTM32™ is a new quality label delivered by ST, which is granted after an evaluation process. It helps engineers identify third party solutions with the highest level of integration and quality for the STM32 microcontrollers' ecosystem. MadeForSTM32™ is offered to members of the ST Partner Program who want to go one step further in our collaboration, with the overall objective of contributing to a high-quality STM32 ecosystem.
The STM32Cube.AI is an extension pack of the widely used STM32CubeMX configuration and code generation tool enabling AI on STM32 Arm® Cortex®-M-based microcontrollers.
The X-CUBE-VS4A Expansion Package consists of a set of libraries and application examples for STM32F7 Series microcontrollers acting as Alexa-enabled devices. It runs on the 32F769IDISCOVERY board, which provides a native Ethernet interface.