The FP-AI-MONITOR2 function pack helps to jump-start the edge AI implementation and development for sensor-monitoring-based applications powered by X-CUBE-AI or NanoEdge™ AI Studio . It covers the entire design of the machine learning development workflow from the data set acquisition to the integration on a physical node. The examples provided allow the user to create, in a matter of minutes, a proof of concept for a predictive maintenance solution with anomaly detection and classification based on both vibration and ultrasound, but also on activity recognition. These examples can be fine-tuned to fit the user's dedicated use cases by retraining the models with the user's data set.
X-CUBE-AI extends the STM32CubeMX capabilities with the automatic conversion of pretrained a neural network and the integration of the generated optimized library into the user's project. The support vector classifier used for human activity recognition (HAR) example is generated by X-CUBE-AI.
NanoEdge™ AI Studio (NanoEdgeAIStudio) automates the creation of autonomous machine learning libraries with the possibility of running training and inference directly on the target. For instance, condition-based monitoring applications using vibration and motion data can be created easily by recompiling the function pack with NanoEdge™ AI anomaly detection libraries.
FP-AI-MONITOR2 runs the learning session and the inference in real time on the STM32U585AI ultra-low-power microcontroller of the STEVAL-STWINBX1 SensorTile wireless industrial node, taking physical sensor data as input.
FP-AI-MONITOR2 implements a wired interactive CLI to configure the node, and manage the learn and detect phases. For simple operation in the field, a standalone battery-operated mode allows basic controls through the user button, without using the console.