Application Software Pack: ML-Based System State Monitor

APP-SW-PACK-ML-STATE-MONITOR

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This Machine Learning (ML) application software pack, ML-based System State Monitor, relies on the Deep Learning (DL) subfield of ML and enables developers to develop and deploy neural networks on MCU-based systems for building smart sensing applications. This example software package, designed to show how to create a fan vibration state monitoring solution and also provides details on how to validate and evaluate the performance of a model by running it through different inference engines on an embedded sensing device.

This App SW Pack is available for the i.MX RT1170 family of Crossover MCUs, LPC55S6x and K66 families of general purpose MCUs.

To access this software pack, click on the download button here to visit the NXP MCUXpresso SDK github repository and follow the steps found in this App SW Pack ML State Monitor Lab Guide.

Software Details

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Diagram

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ML-based System State Monitor - App SW Pack

ML-based System State Monitor - App SW Pack

ML-based System State Monitor - App SW Pack - Example

ML-Based System State Monitor App SW Pack - Example

ML-based System State Monitor - App SW Pack - Collaterals

ML-Based System State Monitor App SW Pack - Collaterals

ML-based System State Monitor - App SW Pack - SW Stack Block Diagram

ML-Based System State Monitor App SW Pack - SW Stack Block Diagram
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Features

  • AI edge Computing
  • eIQ® machine learning (ML) software development and deep learning at the edge on i.MX RT Crossover MCUs
  • Complete and easy ML-based applications development, validation and performance analysis
  • Multiple inference engines usage: TensorFlow™ Lite Micro, DeepViewRT™, Glow
  • Provides examples through an entire workflow of building and deploying on embedded targets with a real use-case
  • Related application spaces: system state monitoring, activity recognition, machine health (preventive maintenance, anomaly detection, failure identification)
  • Low-latency real-time system monitoring and failure identification
  • Applicable to time series real-time data
  • Can be used for other types of sensor data models that detect vibration

Supported Devices

  • i.MX-RT1170: i.MX RT1170 Crossover MCU Dual-Core Arm® Cortex®-M7 and Cortex-M4 Operating Up to 1 GHz
  • LPC55S6x: High Efficiency Arm® Cortex®-M33-Based Microcontroller Family
  • K66_180: Kinetis® K66-180 MHz, Dual High-Speed and Full-speed USBs, 2MB Flash Microcontrollers (MCUs) based on Arm® Cortex®-M4 Core
  • FXLS8974CF: ±2g/±4g/±8g/±16g, Low-Power 12-Bit Digital IoT Accelerometer

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  • Embedded Software - miscellaneous

    Application Software Pack - ML State Monitor

    EXTERNALRev 0Jan 18, 2022 16:36:00APPLICATION-SW-PACK-ML-STATE-MONITOR
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Hardware

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