Motor Health (Thermal) Monitoring Digital Twin
Team
- E/20/037, L.R.H.I. Bandara, e20037@eng.pdn.ac.lk
- E/20/363, S.D.Y.V Senanayake, e20363@eng.pdn.ac.lk
- E/20/365, C.K. Senaratne, e20365@eng.pdn.ac.lk
- E/20/420, J.K. Wanasinghe, e20420@eng.pdn.ac.lk
Supervisor
- Prof. Kamalanath Samarakoon, kamalanath@eng.pdn.ac.lk
Tags
- DigitalTwin
- Grafana
- IIOT
- TinyML
- ESP32
- NodeRed
- InfluxDB
- IndustrialSystems
- Automation
- SCADA
- Monitoring
Table of Contents
Introduction
This project implements an Industrial IoT digital twin for motor health monitoring using a 4-layer edge-to-cloud architecture. The core objective is to detect motor overheating, thermal runaway, and cooling failure using edge-level temperature analytics and cloud-based thermal trend analysis.
System Overview
The implementation uses the following technology stack:
- Edge Data Source (current): Python mock temperature publisher
- Edge Device (future phase): ESP32-S3 firmware layer
- Message Broker: Eclipse Mosquitto (MQTT + Sparkplug B)
- Flow Logic: Node-RED
- Historian: InfluxDB
- Visualization and Digital Twin: Grafana
- Infrastructure: Docker and Docker Compose