Predictive Maintenance System for AC Induction Motor Machine Using ESP32, MySQL and Node-RED Dashboard
Specifications
- Code
- RA2622
- Department
- DKR
- Technology
- ESP32 · Node-RED · MySQL
- Theme
- Industrial Automation
- Student
- Zamirul Ammar bin Aslziam
- Supervisor
- Siti Hajar bt Raman
Objectives
To design a predictive maintenance system using an ESP32 microcontroller to monitor motor condition based on vibration and temperature. To store sensor data wirelessly in a NoSQL (MongoDB) database and display it on a Node-RED dashboard. To provide early warning through the Node-RED dashboard and Telegram notifications when abnormal conditions are detected.
Scope of project
Monitors an AC induction motor using two main parameters: vibration (MPU6050) and temperature (MLX90614). ESP32 reads and processes the sensor data and sends it wirelessly over WiFi. Sensor data is stored in a NoSQL (MongoDB) database. Node-RED dashboard shows live data, sets thresholds and triggers Telegram alerts.
Project description
Problem: AC induction motors can fail without warning, and maintenance on fixed schedules ignores actual motor condition, causing costly downtime. Solution: An ESP32 reads vibration and temperature, sends the data over WiFi to a MongoDB database, and Node-RED displays it live and sends a Telegram alert when readings become abnormal. Workflow: Planning & design → circuit development → software development → system integration & test → enclosure & documentation. Conclusion: Automated data collection and analysis reduces downtime and improves motor reliability. REFERENCES [1] Mobley, R. K. (2002). An Introduction to Predictive Maintenance, 2nd ed. Butterworth-Heinemann. [2] Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process., 20(7), 1483–1510. [3] Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett., 3, 18–23. [4] Carvalho, T. P., et al. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng., 137, 106024. [5] ISO 20816-1:2016. Mechanical vibration — Measurement and evaluation of machine vibration — Part 1: General guidelines.