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The FP-AI-MONITOR1 function pack helps to jump-start the edge AI implementation and development for sensor-monitoring-based applications designed with X-CUBE-AI or with the NanoEdge™ AI Studio . It covers the entire design of the Machine Learning cycle 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 is an STM32Cube Expansion Package part of the STM32Cube.AI ecosystem. It extends the STM32CubeMX capabilities with the automatic conversion of pretrained Neural Network or Machine Learning models and the integration of the generated optimized library into the user's project. X-CUBE-AI offers also several means to validate AI models both on desktop PC and STM32, as well as to measure performance on STM32 devices without user handmade specific C code. The support vector classifier used for human activity recognition (HAR) example is generated by X-CUBE-AI. Other applications can be created using optimized ML and DNN code generated by X-CUBE-AI.

NanoEdge™ AI Studio (NanoEdgeAIStudio) simplifies the creation of autonomous Machine Learning libraries with the possibility of running training on target and inference on the edge. 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 models.

FP-AI-MONITOR1 runs the learning session and the inference in real time on an STM32L4R9ZI ultra-low-power microcontroller (Arm® Cortex®‑M4 at 120 MHz with 2 Mbytes of flash memory and 640 Kbytes of SRAM), taking physical sensor data as input. The SensorTile wireless industrial node (STEVAL-STWINKT1B) embeds industrial-grade sensors, including very high frequency audio and ultrasound spectra detection, 6-axis IMU, 3-axis accelerometer, and vibrometer to record any inertial and vibrational data with high accuracy at high frequencies.

FP-AI-MONITOR1 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.

  • 所有功能

    • Application example of human activity classification based on motion sensors
    • Application example of combined anomaly detection based on vibration and anomaly classification based on ultrasound
    • Complete firmware to program an STM32L4+ sensor node for an AI-based sensor monitoring application on the STEVAL-STWINKT1B 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 NanoEdge™ AI 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-STWINKT1B 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 STM32L4+ 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


Nanoedge AI™ Studio