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.
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.