Condition Monitoring at the Edge using WizFi360-EVB-Pico
A low-power device that can monitor the health of Electro-Mechanical systems at the edge.
WIZnet - WizFi360-EVB-Pico
analog-devices - EVAL-ADXL1003
It is EVAL-ADXL355Z please note. The device was not available in the list.
Software Apps and online services
MathWorks - Thingspeak
Whenever we think of Artificial Intelligence and Machine Learning; What comes to our mind? Maybe Powerful Computers, Huge Servers/Data, and a lot of Computation Power. But Edge AI uses very Low Power devices that can be operated with a battery, working for years with just a single charge.
Conventional maintenance techniques in the industry require a technician to visit the site and check for any failures. This is done periodically. But what if the machines are placed in a remote environment(sky-high power lines or deep under the ground down machinery)? In those cases, Conditioning monitoring with IoT would come into play. Tiny ML at the edge acts as icing on Top when you don't have to send each and every data to the cloud for analysis and waste power. You can perform inference with a lightweight ML model on a low-power microcontroller and predict whether any fault. The combination of Sensors and Machine Learning helps create an early warning system.
WIZnet WizFi360-EVB-Pico is based on Raspberry Pi RP2040 and adds Wi-Fi connectivity using WizFi360. It is pin compatible with the Raspberry Pi Pico board and can be used for IoT Solution development. It would be a good choice to develop my prototype - Condition Monitoring Setup with an Agri Motor Pump.
Prototype for Demonstration
This device does not require any intrusive mechanism to offer Condition monitoring. It can be attached to the external surface and you are good to go.
This project is an Accelerometer-based Condition Monitoring system that predicts the health of the Machine with the help of vibrational data. To accomplish this, I chose to go with Analog Devices EVAL-ADXL355Z Sensor.
The Project had 3 Phases:
1. Collection of Dataset
2. Live Inference
3. Connection with Thingspeak
Collection of Dataset
Wiznet WizFi360-EVB-Pico does support Arduino IDE but I chose to go with PlatformIO. In order to interface with EVAL-ADXL355Z I opted for the NO-OS drivers I made the connections with the sensor as shown in the diagram below. Data collection code: ADXL355 is a separate firmware to fetch the accelerometer values of the X, Y, and Z axes. It pushes these values onto the serial terminal, which is then picked up by the Edge Impulse Data-forwarder tool. The data-acquisition frequency was 85Hz.
I have collected data for 4 Scenarios:
1. Normal-Flow: Proper water supply without air bubbles.
2. Bubbly_Airlflow: Presence of bubbles in water flow.
3. Air_Flow: Presence of no water. The motor should stop.