FP-AI-MONITOR1 getting started

Delivery for this function pack is being prepared

Sensing and condition monitoring are two of the major components of the IoT and predictive maintenance systems, enabling context awareness, production performance improvement, maintenance cost reduction and a drastic decrease of the downtime due to routine maintenance.

The FP-AI-MONITOR1 function pack helps to jump-start the implementation and development for sensor-monitoring-based applications designed with the X-CUBE-AI Expansion Package for STM32Cube 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.

X-CUBE-AI is an STM32Cube Expansion Package part of the STM32Cube.AI ecosystem and extending STM32CubeMX capabilities with automatic conversion of pre-trained Neural Network or Machine Learning models and integration of generated optimized library into the user's project. The X-CUBE-AI Expansion Package offers also several means to validate AI models both on desktop PC and STM32, as well as measure performance on STM32 devices without user handmade ad hoc 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 ML and DNN code generated by X-CUBE-AI.

NanoEdge™ AI Studio 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 re-compiling the function pack with NanoEdge™ AI anomaly detection models.

FP-AI-MONITOR1 runs 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 6-axis IMU, 3-axis accelerometer and vibrometer to record any inertial and vibrational data with high accuracy at high frequencies.

The NanoEdge™ AI library generation itself is out of the scope of this function pack and must be generated using NanoEdge™ AI Studio.

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

This article provides an overview of the following topics:

• The required hardware and software,
• Pre-requisites and setup,
• FP-AI-MONITOR1 console application,
• Running a classification application for sensing on the device,
• Running condition monitoring application using NanoEdge AI libraries on the device,
• Performing the vibration sensor data collection using a prebuilt binary of FP-SNS-DATALOG1,
• Button operated modes, and
• Some links to useful online resources, to help the user better understand and customize the project for her/his own needs.

This article is just to serve as the quick starting guide and for full FP-AI-MONITOR1 user instructions, readers are invited to please refer to FP-AI-MONITOR1 User Manual.

Information

NOTE: The NanoEdge™ library generation itself is out of the scope of this function pack and must be generated using NanoEdge™ AI Studio.

1. Hardware and software overview

1.1. SensorTile wireless industrial node Evaluation Kit STEVAL-STWINKT1B

The STWIN SensorTile wireless industrial node (STEVAL-STWINKT1B) is a development kit and reference design that simplifies the prototyping and testing of advanced industrial IoT applications such as condition monitoring and predictive maintenance. It is powered with an ultra-low-power Arm^® Cortex^®-M4 MCU at 120 MHz with FPU, 2048-Kbyte Flash memory (STM32L4R9). STWIN SensorTile is equipped with a microSD™ card slot for standalone data logging applications. STWIN SensorTile also features a wide range of industrial IoT sensors including:

• an ultra-wide bandwidth (up to 6 kHz), low-noise, 3-axis digital vibration sensor (IIS3DWB)
• a 6-axis digital accelerometer and gyroscope iNEMO inertial measurement unit (IMU) with machine learning core (ISM330DHCX)

and much more. Please refer to this link for all the sensors and features supported by STWIN. Other attractive features include:

• a 480 mAh Li-Po battery to enable standalone working mode
• STLINK-V3MINI debugger with programming cable to flash the board
• a plastic box for ease of placing and planting the SensorTile on the machines for condition monitoring. For further details, visit this link

1.2. FP-AI-MONITOR1 software description

The top-level architecture of the FP-AI-MONITOR1 function pack is shown in the following figure.

2. Prerequisites and setup

2.1. Hardware prerequisites and setup

To use the FP-AI-MONITOR1 function pack on STEVAL-STWINKT1B, the following hardware items are required:

• STEVAL-STWINKT1B development kit board,
• Windows^® powered laptop/PC (Windows^® 7, 8, or 10),
• Two Micro-USB cables, one to connect the sensor-board to the PC, and another one for the STLINK-V3MINI, and
• an STLINK-V3MINI.

2.2. Software requirements

2.2.1. FP-AI-MONITOR1

• Download the FP-AI-MONITOR1 package from ST website, extract and copy the .zip file content into a folder on your PC. The package contains binaries and source code for the sensor board STEVAL-STWINKT1B.

2.2.2. IDE

• Install one of the following IDEs:

  • STMicroelectronics STM32CubeIDE version 1.7.0
  • IAR Embedded Workbench for Arm (EWARM) toolchain version 8.50.9 or later
  • RealView Microcontroller Development Kit (MDK-ARM) toolchain version 5.31

Information

All the steps presented in this document are carried out with STM32CubeIDE, but any of the other two IDEs could have been used.

2.2.3. STM32CubeProgrammer

• STM32CubeProgrammer (STM32CubeProg) is an all-in-one multi-OS software tool for programming STM32 products. It provides an easy-to-use and efficient environment for reading, writing and verifying device memory through both the debug interface (JTAG and SWD) and the bootloader interface (UART, USB DFU, I2C, SPI, and CAN). STM32CubeProgrammer offers a wide range of features to program STM32 internal memories (such as Flash memory, RAM and OTP) as well as external memories.
• This software can be downloaded from STM32CubeProg.

2.2.4. TeraTerm

• TeraTerm is an open-source and freely available software terminal emulator, which is used to host the CLI of the FP-AI-MONITOR1 through a serial connection.
• Download and install the latest version available from TeraTerm.

2.2.5. STM32CubeMX

STM32CubeMX is a graphical tool that allows a very easy configuration of STM32 microcontrollers and microprocessors, as well as the generation of the corresponding initialization C code for the Arm® Cortex®-M core or a partial Linux® Device Tree for Arm^® Cortex^®-A core), through a step-by-step process. Its salient features include:

• Intuitive STM32 microcontroller and microprocessor selection.
• Generation of initialization C code project, compliant with IAR™, Keil^® and STM32CubeIDE (GCC compilers) for Arm^® Cortex^®-M core
• Development of enhanced STM32Cube Expansion Packages thanks to STM32PackCreator, and
• Integration of STM32Cube Expansion Packages into the project.

For downloading and details of all the features please visit st.com.

2.2.6. X-CUBE-AI

X-CUBE-AI is an STM32Cube Expansion Package part of the STM32Cube.AI ecosystem and extending STM32CubeMX capabilities with automatic conversion of pre-trained Artificial Intelligence models and integration of generated optimized library into the user's project. The easiest way to use it is to download it inside the STM32CubeMX tool (version 7.0.0 or newer) as described in user manual Getting started with X-CUBE-AI Expansion Package for Artificial Intelligence (AI) (UM2526). The X-CUBE-AI Expansion Package also offers several means to validate the AI models (Neural Network and Scikit-Learn models) both on desktop PC and STM32, as well as measure performance on STM32 devices (computational and memory footprints) without user handmade ad-hoc C code.

2.2.7. Python^TM 3.7.X

Python is an interpreted high-level general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant indentation. Its language constructs as well as its object-oriented approach aim at helping programmers write clear, logical code for small and large-scale projects. To build and export the Onnx models, set up a Python environment with a list of packages. The list of the required packages along with their versions can be found as a text file in the package at location /FP-AI-MONITOR1/Utilities/AI_resources/requirements.txt. The following command can be used in the command terminal of the anacondas prompt or Ubuntu to install all the packages specified in the configuration file requirements.txt:

pip install -r requirements.txt

2.2.8. NanoEdge AI Studio

The function pack lets the user use the AI libraries generated by NanoEdge^TM AI Studio powered by Cartesiam. Generate the libraries from the NanoEdge AI Studio and then embed these libraries in the FP-AI-MONITOR1. The Studio can be downloaded from Cartesiam.ai as a free trial version for the STMicroelectronics boards.

2.3. Installing the function pack to STWIN

2.3.1. Getting the function pack

The first step is to get the function pack. Download the FP-AI-MONITOR1 from ST website. Once the pack is downloaded, unpack/unzip it and copy the content to a folder on the PC. The steps of the process along with the content of the folder are shown in the following image.

2.3.2. Flashing the application on the sensor board STEVAL-STWINKT1B

Once the package has been downloaded and unpacked, the next step is to program the sensor node with the binary of the function pack. For the convenience of the users, the function pack is equipped with a pre-built binary file of the project. This binary file can be found at path /FP-AI-MONITOR1/Projects/Binary/FP-AI-MONITOR1.bin. The sensor board can be very easily programmed with the provided binary by simply performing a drag-and-drop action as shown in the figure below.

3. FP-AI-MONITOR1 console application

FP-AI-MONITOR1 provides an interactive command-line interface (CLI) application. This CLI application equips a user with the ability to configure and control the sensor node, to perform learning (for NanoEdge™ AI libraries), and (anomaly) detection operations on the edge. The following sections provide a small guide on how to install this CLI application on a sensor board and control it through the CLI from TeraTerm.

3.1. Setting up the console

Once the sensor board is programmed with the binary of the project (as shown in section 2), the next step is to set up the serial connection of the board with the PC through TeraTerm. To do so, start TeraTerm and create a new connection by either selecting it from the toolbar or by selecting the proper port to establish the serial communication with the board. In the figure below, this is COM10 - USB Serial Device (COM 10).

Once the connection is established, the message below is displayed. If this is not the case, reset the board.

Typing help shows the list of all the available commands along with their usage instructions.

3.2. Configuring the sensors

Through the CLI interface, a user can configure the supported sensors for sensing and condition monitoring applications. The list of all the supported sensors can be displayed on the CLI console by entering the command sensor_info. This command prints the list of the supported sensors along with their ids as shown in the image below. The user can configure these sensors using these ids. The configurable options for these sensors include:

• enable: to activate or deactivate the sensor,
• ODR: to set the output data rate of the sensor from the list of available options, and
• FS: to set the full-scale range from the list of available options.

The current value of any of the parameters for a given sensor can be printed using the command,

$ sensor_get <sensor_id> <param>

or all the information about the sensor can be printed using the command:

$ sensor_get <sensor_id> all

Similarly, the values for any of the available configurable parameters can be set through the command:

$ sensor_set <sensor_id> <param> <val>

The figure below shows the complete example of getting and setting these values along with old and changed values.

4. Classification applications for sensing

The CLI application comes with a prebuilt Human Activity Recognition model. This functionality can be started by typing the command:

$ start ai

Note that the provided HAR model is built with a dataset created using the IHM330DHCX_ACC sensor with ODR = 26, and FS = 4. To achieve good performance, the user is required to set these parameters to the sensor configurations using the instructions provided in section Configuring the sensors.

Running the $ start ai command starts the inference on the accelerometer data and predicts the performed activity along with the confidence. The supported activities are:

• Stationary,
• Walking,
• Jogging, and
• Biking.

The following screenshot shows the normal working session of the AI command in CLI application.

5. Condition monitoring using NanoEdge AI Machine Learning library

FP-AI-MONITOR1 includes a pre-integrated stub that can be easily replaced by an AI condition monitoring library generated and provided by NanoEdge AI Studio. Please refer to User Manual for the full details. For further details on how NanoEdge AI libraries work, read the detailed documentation of NanoEdge AI studio.

The provided stub can used to test the NanoEdge AI Studio related functionalities as running learning phase and inference on the edge.

The learning phase can be started by simply issuing a start neai_learn command from the CLI console. Launching this command shows the process of learning and displays a message on the console every time learning is performed on a new signal (see the below snippet):

 
$ start neai_learn
NanoEdgeAI: starting

$ {"signal": 1, "status": success}
{"signal": 2, "status": success}
{"signal": 3, "status": success}
:
:

The process can be stopped by pressing the ESC key on the keyboard.

The learning can also be started by performing a long-press of the user button. Pressing the user button again stops the learning process.

Once the normal conditions are learned, the user can start the condition monitoring process by issuing the command start neai_detect:

$ start neai_detect
NanoEdgeAI: starting

or by double-pressing the user button on the sensor board.

The process can be stopped by pressing the ESC key on the keyboard or alternatively pressing the user button .

6. Data collection

The data collection functionality is out of the scope of this function pack. However, to facilitate the users and equip them with the possibility to perform a datalog, a precompiled .bin file from FP-SNS-DATALOG1 is provided in the Utilities and can be found under path /FP-AI-MONITOR1_V1.0.0/Utilities/Datalog/. To acquire the data from any of the available sensors on the sensor-board, the user can program the sensor-board with this binary using the drag-drop method shown in section 2.3.2.

Information

For data logging using the high-speed datalogger, the user needs a FAT32-FS formatted microSDTM card.

Once the sensor-board is programmed, the data can be logged using the following instructions.

• Place a DeviceConfig.json file with the configurations of the sensors to be used for the datalogging in the root folder of the microSD^TM card. Some sample .json files are provided in the package and can be found at the location FP-AI-MONITOR1_V1.0.0/Utilities/Datalog/Sample_STWIN_Config_Files/. These files are to be named exactly DeviceConfig.json.
• Insert the SD card into the STWIN board.
• Reset the board. Orange LED blinks once per second. The custom sensor configuration provided in DeviceConfig.json is loaded from the file.
• Press the [USR] button to start data acquisition on the SD card. The orange LED turns off and the green LED starts blinking to signal sensor data are being written into the SD card.
• Press the [USR] button again to stop data acquisition. Do not unplug the SD card or turn the board off before stopping the acquisition or the data on the SD card will be corrupted.
• Remove the SD card and insert it into an appropriate SD card slot on your PC. The log files are stored in STWIN_### folders for every acquisition, where ### is a sequential number determined by the application to ensure log file names are unique. Each folder contains a file for each active subsensor called SensorName_subSensorName.dat. Each file contains raw sensor data coupled with timestamps, a DeviceConfig.json with specific information about the device configuration that are necessary for correct data interpretation, and an AcquisitionInfo.json with information about the acquisition.

Information

For details on how to use all the features of the provided HSDatalog.bin binary, users are invited to refer to the user manual of FP-SNS-DATALOG1.

7. Button operated modes

To facilitate the usage, the FP-AI-MONITOR1 is equipped with a button-operated mode. The purpose of the button-operated mode is to enable the FP-AI-MONITOR1 to be operated even in the absence of the CLI console. All the (default) values required for running different functions are provided in the firmware. In button-operated mode, the user can start/stop different execution phases by using only the user button. The following table shows the user instruction for the button-operated mode.

Button Press	Description	Action
SHORT_PRESS	The button is pressed for less than 200 ms and released	Starts AI inferences for X-CubeAI model.
LONG_PRESS	The button is pressed for more than 200 ms and released	Starts the learning for NanoEdgeTM AI Library.
DOUBLE_PRESS	A succession of two SHORT_PRESS in less than 500 ms	Starts the inference for NanoEdgeTM AI Library.
ANY_PRESS	The button is pressed and released (overlaps with the three other modes)	Stops the current running execution phase.

Warning

LED functionality needs to be agreed since it cannot be applied as described below : X-CUBE-AI model is now HAR and no more anomalie detection a proposal has been logged in tracker.

The onboard LEDs indicate the status of the current execution phase. The LEDs are allocated as shown in the table below:

Pattern	Green	Orange
OFF	Power OFF
ON	idle	System error
BLINK	X-CUBE-AI detecting, no anomaly	X-CUBE-AI anomaly detected
BLINK_SHORT	NanoEdgeTM AI detecting, no anomaly	NanoEdgeTM AI anomaly detected
BLINK_LONG	NanoEdgeTM AI learning, status OK	NanoEdgeTM AI learning, status FAILED

8. Documents and related resources

Warning

Note: Need to fix the links once available and checked with Laurent.

• Put Data brief of FP-AI-MONITOR1 Link: Multi-sensor AI data monitoring framework on wireless industrial node, function pack for STM32Cube
• User Manual : User Manual for FP-AI-MONITOR1
• STEVAL-STWINKT1B
• STM32CubeMX : STM32Cube initialization code generator
• X-CUBE-AI  : expansion pack for STM32CubeMX
• NanoEdge AI Studio: NanoEdge AI™ by Cartesiam the first Machine Learning Software, specifically developed to entirely run on microcontrollers.
• DB4345: Data brief for STEVAL-STWINKT1B.
• UM2777: How to use the STEVALSTWINKT1B SensorTile Wireless Industrial Node for condition monitoring and predictive maintenance applications.