This research presents an Industrial IoT-based health monitoring system tailored for motor and transformer components in industrial environments. Integrating advanced sensors, microcontrollers, and MQTT communication, the system enables real-time monitoring and predictive maintenance. Accurate measurement of critical parameters, coupled with effective anomaly detection algorithms, empowers operators to pre-emptively address potential equipment failures. The system's reliability and effectiveness in optimizing equipment reliability and minimizing downtime offer a promising solution for industrial maintenance challenges. The system consists of a network of sensors that are positioned strategically on motor and transformer components in order to gather essential operating data. These sensors are interfaced with microcontrollers, which then use MQTT to send the processed and analyzed data to a central monitoring station. Our system makes use of MQTT communication's efficiency and lightweight design to guarantee timely delivery of important information, allowing operators to make decisions quickly.

Keywords: Industrial IoT, Health Monitoring System, Motor Components, Transformer Components,
ESP32, ACS758, ADXL345, LM35, MQTT Integration, Predictive Maintenance

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1. Kwon, D., Hodkiewicz, M. R., Fan, J., Shibutani, T., & Pecht, M. G. (2016). IoT-based prognostics and systems health management for industrial applications. IEEE access, 4, 3659-3670.
2. Hanafi, D., & Aziz, Z. (2022). Health Monitoring System for Transformer by using Internet of Things (IoT). International Journal of Electrical, Energy and Power System Engineering, 5 (1), 19-23.
3. Es-Sakali, N., Cherkaoui, M., Mghazli, M. O., & Naimi, Z. (2022). Review of predictive maintenance algorithms applied to HVAC systems. Energy Reports, 8, 1003-1012.
4. Jaloudi, S. (2019). Communication protocols of an industrial internet of things environment: A comparative study. Future Internet, 11 (3), 66.
5. Zheng, H., Feng, Y., Gao, Y., & Tan, J. (2018). A robust predicted performance analysis approach for data-driven product development in the industrial internet of things. Sensors, 18(9), 2871.
6. Zhang, W., Yang, D., Xu, Y., Huang, X., Zhang, J., & Gidlund, M. (2020). DeepHealth: A self-attention based method for instant intelligent predictive maintenance in industrial Internet of Things. IEEE Transactions on Industrial Informatics, 17 (8), 5461-5473.
7. Seetharaman, A., Patwa, N., Saravanan, A. S., & Sharma, A. (2019). Customer expectation from industrial internet of things (IIOT). Journal of Manufacturing Technology Management, 30(8), 1161-1178.
8. Chhaya, L., Sharma, P., Kumar, A., & Bhagwatikar, G. (2018). IoT-based implementation of field area network using smart grid communication infrastructure. Smart Cities, 1(1), 176-189.
9. Chen, J. C., Chen, T. L., Liu, W. J., Cheng, C. C., & Li, M. G. (2021). Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Advanced Engineering Informatics, 50, 101405.
10. Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2020). Industrial internet of things: Recent advances, enabling technologies and open challenges. Computers & electrical engineering, 81, 106522.

This research presents an Industrial IoT-based health monitoring system tailored for motor and transformer components in industrial environments. Integrating advanced sensors, microcontrollers, and MQTT communication, the system enables real-time monitoring and predictive maintenance. Accurate measurement of critical parameters, coupled with effective anomaly detection algorithms, empowers operators to pre-emptively address potential equipment failures. The system's reliability and effectiveness in optimizing equipment reliability and minimizing downtime offer a promising solution for industrial maintenance challenges. The system consists of a network of sensors that are positioned strategically on motor and transformer components in order to gather essential operating data. These sensors are interfaced with microcontrollers, which then use MQTT to send the processed and analyzed data to a central monitoring station. Our system makes use of MQTT communication's efficiency and lightweight design to guarantee timely delivery of important information, allowing operators to make decisions quickly.

Keywords: Industrial IoT, Health Monitoring System, Motor Components, Transformer Components,
ESP32, ACS758, ADXL345, LM35, MQTT Integration, Predictive Maintenance

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Kwon, D., Hodkiewicz, M. R., Fan, J., Shibutani, T., & Pecht, M. G. (2016). IoT-based prognostics and systems health management for industrial applications. IEEE access, 4, 3659-3670.
2. Hanafi, D., & Aziz, Z. (2022). Health Monitoring System for Transformer by using Internet of Things (IoT). International Journal of Electrical, Energy and Power System Engineering, 5 (1), 19-23.
3. Es-Sakali, N., Cherkaoui, M., Mghazli, M. O., & Naimi, Z. (2022). Review of predictive maintenance algorithms applied to HVAC systems. Energy Reports, 8, 1003-1012.
4. Jaloudi, S. (2019). Communication protocols of an industrial internet of things environment: A comparative study. Future Internet, 11 (3), 66.
5. Zheng, H., Feng, Y., Gao, Y., & Tan, J. (2018). A robust predicted performance analysis approach for data-driven product development in the industrial internet of things. Sensors, 18(9), 2871.
6. Zhang, W., Yang, D., Xu, Y., Huang, X., Zhang, J., & Gidlund, M. (2020). DeepHealth: A self-attention based method for instant intelligent predictive maintenance in industrial Internet of Things. IEEE Transactions on Industrial Informatics, 17 (8), 5461-5473.
7. Seetharaman, A., Patwa, N., Saravanan, A. S., & Sharma, A. (2019). Customer expectation from industrial internet of things (IIOT). Journal of Manufacturing Technology Management, 30(8), 1161-1178.
8. Chhaya, L., Sharma, P., Kumar, A., & Bhagwatikar, G. (2018). IoT-based implementation of field area network using smart grid communication infrastructure. Smart Cities, 1(1), 176-189.
9. Chen, J. C., Chen, T. L., Liu, W. J., Cheng, C. C., & Li, M. G. (2021). Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Advanced Engineering Informatics, 50, 101405.
10. Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2020). Industrial internet of things: Recent advances, enabling technologies and open challenges. Computers & electrical engineering, 81, 106522.