Product overview
Key Benefits
Jump-start the implementation of your sensor-monitoring-based applications
Find application examples of anomaly detection and classification based on both vibration and ultrasound, but also on activity recognition based on motion sensors.
Predictive maintenance on ultra-low power STM32 in a matter of minutes
Provides a complete firmware to program an STM32U5 sensor node on the STEVAL-STWINBX1 SensorTile wireless industrial node.
Description
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.
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All features
- Application example of combined anomaly detection based on vibration and anomaly classification based on ultrasound
- Application example of human activity classification based on motion sensors
- Complete firmware to program an STM32U5 sensor node for an AI-based sensor monitoring application on the STEVAL-STWINBX1 SensorTile wireless industrial node
- Runs classical machine learning (ML) and artificial neural network (ANN) models generated by the X-CUBE-AI, an STM32Cube Expansion Package
- Runs machine learning (ML) libraries generated by NanoEdge™ AI Studio (NanoEdgeAIStudio) for AI-based sensing applications. Easy integration by replacing the preintegrated substitute
- Application binary of high-speed datalogger for STEVAL-STWINBX1 data record from any combination of sensors and microphones configured up to the maximum sampling rate on a microSD™ card
- eLooM (embedded Light object-oriented fraMework) enabling efficient development of soft real-time, multitasking, event-driven embedded applications on STM32U5 Series microcontrollers
- Sensor manager eLooM component to configure any board sensors easily, and suitable for production applications
- Digital processing unit (DPU) eLooM component providing a set of processing blocks, which can be chained together, to apply mathematical transformations to the sensors data
- Configurable autonomous mode controlled by user button
- Interactive command-line interface (CLI):
- Node and sensor configuration
- Configuration of applications running either an X-CUBE-AI ML or ANN model, or a NanoEdge™ AI Studio (NanoEdgeAIStudio) model with learn-and-detect capability
- Configuration of applications running concurrently an X-CUBE-AI ANN model, and a NanoEdge™ AI Studio model with learn-and-detect capability
- Configuration of applications running a NanoEdge™ AI Studio model with classification capability
- Easy portability across STM32 microcontrollers by means of the STM32Cube ecosystem
- Free and user-friendly license terms
