FP-AI-PREDMNT2

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STM32Cube function pack for STEVAL-STWINKT1B evaluation kit plus STEVAL-STWINWFV1 Wi-Fi adapter board for predictive maintenance application based on artificial intelligence (AI)

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Product overview

Description

FP-AI-PREDMNT2 is an STM32Cube function pack that programs the STWIN as an IoT Edge node, connected to the cloud, able to acquire sensor data, process them and send the results to the DSH-PREDMNT cloud dashboard. It includes dedicated algorithms for advanced time and frequency domain signal processing and analysis of 3D digital accelerometers with flat bandwidth up to 6 kHz.

The function pack helps to jump-start the implementation and development of condition monitoring applications designed with the NanoEdge™ AI Studio solution by Cartesiam, thus easily enabling an AI-based predictive maintenance solution (the NanoEdge™ AI library generation is out of the scope of this function pack and must be generated using NanoEdge™ AI Studio).

The package includes pressure, relative humidity and temperature sensor monitoring, as well as audio algorithms to check acoustic emission (AE), up to 20 kHz, and ultrasound emission analysis up to 80 kHz.

Using the STBLESensor app you can set up Wi-Fi credentials and exchange cloud certificates to enable the connection to the dedicated DSH-PREDMNT web-based dashboard. The dashboard allows monitoring and logging the algorithm output, sensor data and equipment status.

The FP-AI-PREDMNT2, together with the suggested combination of STM32 and ST devices, can be used to develop specific industrial predictive maintenance applications for early detection of warning signs of potential failure.

The software runs on the STM32 microcontroller and includes all the necessary drivers for the STEVAL-STWINKT1B evaluation kit.

FP-AI-PREDMNT2 firmware is based on application-level modules (Sensor Manager, Digital Processing Units, etc.) that you can reuse and easily extend to build a customized application.

These application modules adopt state-of-the-art design patterns and natively support low-power modes. To enable this solution, the function pack has been built on top of eLooM, an embedded Light object-oriented fraMework for STM32 applications specifically designed for embedded low-power applications powered by STM32.
  • All features

    • Complete firmware to develop a sensor node for predictive maintenance applications, featuring analog microphone, environmental and motion sensors, and performing real-time monitoring of parameters and equipment status via Wi-Fi connectivity
    • Compatible with NanoEdge™ AI Studio solution by Cartesiam, to enable AI-based solution
    • Generic FFT library middleware to enable frequency domain analysis for any kind of sensor through Fast Fourier Transform (with programmable size, overlapping and windowing)
    • Motion TD library middleware for vibration analysis in time domain (speed RMS and acceleration peak)
    • Configurable alarm and warning thresholds for key parameters
    • Compatible with STBLESensor application for Android/iOS, to perform Wi-Fi configuration and secure certificate provisioning
    • Compatible with DSH-PREDMNT web-based predictive maintenance dashboard for monitoring sensor data and device status
    • Easy portability across different MCU families, thanks to STM32Cube
    • Firmware modular example based on eLooM (embedded Light object-oriented fraMework for STM32) to enable code re-usability at application level
    • Free, user-friendly license terms

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