An IoT-based Downtime Data Tracking and Alert System to Support OEE Monitoring in Production Lines
DOI:
https://doi.org/10.15282/Keywords:
Downtime Tracking , OEE , IoT-based , PHPMyAdmin , HTML InterfaceAbstract
This project presents the development of a Downtime Data Tracking and Alert System (DDTAS) to enhance the efficiency of technical support teams and machine operators in monitoring and recording production line downtimes, thereby supporting improved monitoring of Overall Equipment Effectiveness (OEE). The project was conducted in collaboration with an industry partner through a Work-Based Learning (WBL) programme. Previously, machine downtimes were recorded manually using note cards, and breakdowns were signaled using emergency lights. This manual approach led to inaccuracies due to inconsistent time logging and reliance on human input. Additionally, repair times were logged separately through online forms, complicating data integration for performance analysis. To address these limitations, DDTAS was developed using the ESP32 microcontroller, RDM6300 RFID reader, PHPMyAdmin, and custom software. The system automates the capture and storage of downtime data, transmitting it to a centralized database and displaying it through an HTML-based web interface. The interface includes a graphical user interface (GUI) that indicates machine status—such as down, under repair, or operational—along with a Gantt chart visualizing downtime duration, repair time, and idle periods. This real-time dashboard, deployed in the technical team’s workspace, enables faster response and more accurate downtime tracking. By replacing manual recording with automated data acquisition and visualization, DDTAS improves the reliability of downtime data, which is critical for calculating OEE metrics. The system ultimately contributes to enhanced operational visibility, more informed decision-making, and continuous improvement in production efficiency.
References
[1] G. G. Maulana, “Production Monitoring System Using Overall Equipment Effectiveness (OEE) Method to Improve Stamping Machine Performance,” J. Polimesin, vol. 20, no. 2, 2024. http://dx.doi.org/10.30811/jpl.v20i2.2560
[2] E. J. Clements, V. Sonwaney, and R. K. Singh, “Measurement of overall equipment effectiveness to improve operational efficiency,” Int. J. Process Manag. Benchmarking, vol. 8, no. 2, pp. 246–260, 2018. http://dx.doi.org/10.1504/IJPMB.2018.10010267
[3] M. Sayuti, J. Juliananda, Syarifuddin, and Fatimah, “Analysis of the Overall Equipment Effectiveness (OEE) to Minimize Six Big Losses of Pulp machine: A Case Study in Pulp and Paper Industries,” IOP Conf. Ser. Mater. Sci. Eng., vol. 536, no. 1, 2019. http://dx.doi.org/10.1088/1757-899X/536/1/012061
[4] A. Eschbach, “Combining OEE and IoT Data to Improve Production,” Automation World, Feb. 4, 2022. https://www.automationworld.com/factory/oee/article/22030929/combining-oee-and-iot-data-to-improve-production
[5] L.M. Tumbajoy, M. Muñoz-Añasco, and S. Thiede, “Enabling Industry 4.0 impact assessment with manufacturing system simulation: an OEE based methodology,” Procedia CIRP, vol. 107, pp. 681-686, 2022. https://doi.org/10.1016/j.procir.2022.05.045
[6] R.C. Hansen, Overall Equipment Effectiveness: A Powerful Production/Maintenance Tool for Increased Profits, First ed. New York: Industrial Press, 2002.
[7] H. N. Dai, H. Wang, G. Xu, J. Wan, and M. Imran, “Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies,” Enterprise Information Systems, vol. 14, no. 9–10, pp. 1279–1303, 2019. https://doi.org/10.1080/17517575.2019.1633689
[8] L. Longard, T. Prein, and J. Metternich, “Intraday forecasting of OEE through sensor data and machine learning,” Procedia CIRP, vol. 120, pp. 93-98, 2023, https://doi.org/10.1016/j.procir.2023.08.017
[9] L. Walker, “How to improve production with overall equipment efficiency,” Plant Engineering, Dec. 17, 2024. https://www.plantengineering.com/how-to-improve-production-with-overall-equipment-efficiency/
[10] C. Mărcuță and MoldStud Research Team, “Enhancing Factory Reliability and Reducing Downtime – The Critical Role of IoT,” MoldStud, Jul. 6, 2025. https://moldstud.com/articles/p-enhancing-factory-reliability-and-reducing-downtime-the-critical-role-of-iot
[11] J. Brodny and M. Tutak, “Applying Sensor-Based Information Systems to Identify Unplanned Downtime in Mining Machinery Operation,” Sensors, vol. 22, no. 6, p. 2127, 2022. https://doi.org/10.3390/s22062127
[12] N.D. Smith, Y. Hovanski, J. Tenny, and S. Bergner, “Digital Performance Management: An Evaluation of Manufacturing Performance Management and Measurement Strategies,” Machines, vo. 12, no. 8, p. 555, Aug. 2022. http://dx.doi.org/10.3390/machines12080555
[13] Y. Won, S. Kim, K.-J. Park, and Y. Eun, “Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study,” Sensors, vol. 21, no. 21, p. 7366, Nov. 2021. https://doi.org/10.3390/s21217366
[14] Mastang, Wilarso, M. A. Pahmi, D. Mugisidi and N. Arsad, “Machine Condition Monitoring System Based on IoT Platform for Intelligent Maintenance,” International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), Yogyakarta, Indonesia, pp. 177-181, 2023. https://doi.org/10.1109/AIRoSIP58759.2023.10873875
[15] Z. A. Zaki et al., “IoT Integrated Conveyor Centralized System,” 5th International Conference on Industrial Engineering and Artificial Intelligence (IEAI), Bangkok, Thailand, pp. 1-7, 2024. https://doi.org/10.1109/IEAI62569.2024.00010
[16] M. Pantelidakis, M. Katsigiannis, K. Mykoniatis, G. Purdy and J. Liu, “Condition Monitoring for Overall Equipment Effectiveness using Internet of Things, Edge Computing, and Extended Reality,” 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Spain, pp. 1-6, 2023. https://doi.org/10.1109/ICECCME57830.2023.10252424
[17] Y. Liu et al., “Deep Anomaly Detection for Time‑series Data in Industrial IoT: A Communication‑Efficient On‑device Federated Learning Approach,” IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6348-6358, April 2021. https://doi.org/10.1109/JIOT.2020.3011726
[18] W. Zhang et al., “Blockchain‑based Federated Learning for Device Failure Detection in Industrial IoT,” IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5926-5937, Sep. 2020. https://doi.org/10.1109/JIOT.2020.3032544
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