IoT Based Health Monitoring System of Transformer

Abstract

This research introduces an IoT-based health monitoring system specifically designed fortransformer components within industrial environments. By integrating advanced sensors, microcontrollers, and the local web server for data visualization, the system enables real-time monitoring and predictive maintenance. The precise measurement of critical parameters, combined with effective anomaly detection algorithms, empowers operators to proactively address potential equipment failures. The system\'s reliability and effectiveness in enhancing equipment dependability and minimizing downtime present a promising solution for industrial maintenance challenges. The system comprises a network of sensors strategically positioned on and transformer components to collect essential operating data. Additionally, a relay-based feedback system is incorporated to execute protection actions when abnormal conditions are detected, ensuring improved equipment safety and reliability. These sensors interface with microcontrollers, which then use the web-based interface to transmit and display the processed and analyzed data to a central monitoring station. The efficiency and user-friendly nature of the web interface ensure the timely delivery of critical information, enabling operators to make quick decisions

Introduction

This study presents an Industrial Internet of Things (IIoT)-based health monitoring system for transformer components to improve equipment reliability, reduce downtime, and enable predictive maintenance in industrial environments. Conventional maintenance methods often fail to detect faults early, resulting in unexpected failures and costly interruptions. To overcome these limitations, the proposed system continuously monitors critical transformer parameters, including current, vibration, temperature, and humidity, using IoT-enabled sensors and performs real-time condition assessment. A relay-based protection mechanism automatically responds to abnormal operating conditions, while a local web interface provides remote access to real-time monitoring data.

The system was developed through a structured methodology involving requirement analysis, system design, hardware integration, firmware development, testing, and performance evaluation. The hardware consists of an ESP32 microcontroller, ACS758 Hall-effect current sensor, ADXL345 three-axis accelerometer, DHT11 temperature and humidity sensor, and a relay module. The ESP32 serves as the central controller, collecting sensor data, executing monitoring algorithms, and communicating with the local web server. Firmware was developed using the Arduino IDE, incorporating data acquisition, anomaly detection, relay control, and HTTP-based communication for real-time visualization.

The software architecture utilizes a local web-based monitoring system instead of cloud platforms, reducing latency and eliminating dependence on continuous internet connectivity. Sensor data are transmitted to the local web server via HTTP, where they are displayed through real-time dashboards and stored for historical analysis. Embedded data analytics algorithms perform anomaly detection, trend analysis, and predictive maintenance, allowing early identification of equipment deterioration before failures occur.

Experimental results demonstrate that the proposed system successfully provides real-time monitoring and predictive maintenance capabilities for transformer components. The integrated sensors accurately measure electrical and environmental parameters, enabling timely detection of abnormal current, excessive vibration, overheating, and unfavorable humidity conditions. The relay module enhances operational safety by automatically disconnecting or protecting the transformer whenever predefined safety thresholds are exceeded, thereby minimizing the risk of equipment damage and unplanned downtime.

Overall, the developed IIoT-based health monitoring system represents an effective solution for improving transformer reliability, operational efficiency, and maintenance practices by shifting from reactive to proactive maintenance. Although the system has limitations related to hardware capability, network dependency, and cybersecurity, future improvements such as additional sensor integration, offline functionality, energy optimization, machine learning-based fault prediction, and enhanced user interfaces can further improve its performance and industrial applicability.

Conclusion

In conclusion, our research has demonstrated the successful development and validation of an innovative Industrial IoT-based health monitoring system for transformer components in industrial environments. By incorporating advanced sensors, microcontrollers, and real-time communication through the Local Web Server, the system provides real-time monitoring and predictive maintenance capabilities. Precise measurement of critical parameters, combined with effective anomaly detection algorithms, enables operators to proactively manage potential equipment failures, reducing downtime and enhancing operational efficiency. Looking ahead, deploying this system in real-world industrial environments is set to transform maintenance practices and significantly enhance equipment reliability.

References

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Copyright

Copyright © 2026 Pravin Mokasare, Saurabh Malwale, Virendra Phule, Prof. R. P. Rajput. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.