README.md

Fan Condition Monitoring using AVR128DA48 Curiosity Nano

Deployed fan condition monitor

This repository is a companion to the Fan Condition Monitoring with SensiML tutorial on the Microchip Developer website. It contains the firmware to classify the operational state of a Honeywell HT-900 fan including whether the fan is on, what speed it's running at, and whether the fan is experiencing one of a handful of fault conditions (tapping, shaking, unknown). The demo project runs on the AVR Curiosity Nano with the Bosch BMI160 (Mikroe IMU2 click board) or TDK ICM42688 (Mikroe IMU14 click board) sensors.

The fan states that the model can recognize (shown in the video above) are summarized below:

• Unknown
• Fan Off
• Tapping
• Fan Speed 1
• Fan Speed 2
• Fan Speed 3
• Shaking

Related Documentation

• AVR® DA Family Product Page

Software Used

• MPLAB® X IDE (>=5.45 Recommended) (microchip.com/mplab/mplab-x-ide)
• MPLAB® XC8 (>=2.32 Recommended) (microchip.com/mplab/compilers)
• MPLAB® Code Configurator (MCC) (>=5.1.0 Recommended) (microchip.com/mplab/mplab-code-configurator)
• MPLAB® Code Configurator (MCC) Device Libraries AVR MCUs (microchip.com/mplab/mplab-code-configurator)
• Microchip AVR-Dx Series Device Support (1.9.119 Recommended) (packs.download.microchip.com)

Hardware Documentation

• AVR128DA48 Curiosity Nano Evaluation Kit (DM164151)
• Curiosity Nano Base for Click boards™ (AC164162)
• IMU 2 click board (mikroe.com/6dof-imu-2-click)
• IMU 14 click board (mikroe.com/6dof-imu-14-click)

Data Collection Firmware

To build data logging firmware for different sensor configurations, visit the ml-avrda-cnano-imu-data-logger repository.

Sensor Configuration

The sensor configuration used in this demo is summarized in the table below. These settings can be changed by modifying app_config.h.

IMU Sensor	Axes	Sampling Rate	Accelerometer Range	Gyrometer Range
Bosch BMI160	Ax, Ay, Az, Gx, Gy, Gz	100Hz	2G	125DPS

Firwmare Operation

The firmware will reflect the state of operation of the demo using the onboard LEDs; this behavior is summarized in the table below.

State	LED Behavior	Description
Fan Off	LED0 on	Fan is not running.
Fan Speed 1/2/3	LED0 low/med/high speed blinking	Fan is running normally.
Shaking	LED0 fast blink	Detected fan shaking.
Tapping/Unknown	LED0 turbo blink	Detected tapping or other abnormal behavior.
Firmware error	LED0 off	Fatal error. (Do you have the correct sensor plugged in?).
Buffer overflow	LED0 lit for 5 seconds	Processing is not able to keep up with real-time; data buffer has been reset.

In addition, the firmware also prints the classification output for each inference over the UART port. To read the UART port output, use a terminal emulator of your choice (e.g., PuTTY) with the following settings:

• Baudrate 115200
• Data bits 8
• Stop bits 1
• Parity None

The terminal output should look similar to the figure shown below.

UART Terminal Output

Note that the output classification is given as an integer number which corresponds to the class ID; the class ID to class label mapping is described in the table below.

Class ID	Class Name
0	Unknown
1	Fan Off
2	Shaking
3	Fan Speed 1
4	Fan Speed 2
5	Fan Speed 3
6	Tapping

Usage with SensiML Open Gateway

The firmware UART output can be visualized with the SensiML Open Gateway application:

1. Open a terminal and change to the directory where you've checked out this repository.
2. Clone the open-gateway repository and install the dependencies:

   git clone https://github.com/sensiml/open-gateway
pip install -r open-gateway/requirements.txt

3. Change the baudrate (BAUD_RATE variable) in open-gateway/config.py to 115200
4. Change to the open-gateway directory and run the open-gateway application, passing in the fan demo's model.json description file:

   cd open-gateway
python app.py -m firmware/knowledgepack/libsensiml/model.json

5. Connect to the Curiosity Nano in the gateway application:
   • Select the Recognition device mode.
   • Select Serial connection type, and enter the UART address (e.g. COM4) in the Device ID field.
   • Click Connect To Device.
6. Switch to the Test Mode tab and click Start Stream.

Firmware Benchmark

Measured with the BMI160 sensor configuration, -O2 level compiler optimizations, and 4MHz clock

• 30kB Flash
• 2.6kB RAM
• 24ms Inference time (average)

Classifier Performance

Below is the confusion matrix result for the classifier evaluated on the entire ht-900 fan condition dataset.

Summary

This example illustrates how you can use the AVR128DA48 for predictive maintenance applications. For more information visit the companion guide for this repository at microchip.wikidot.com/machine-learning:avr-fan-condition-monitoring-with-sensiml.