Abstract
This study presents an in-depth analysis of implementing an Internet of Things (IoT)-based system for real-time condition monitoring and control of DC brushed motors. The objective is to enhance industrial motor performance monitoring through efficient and precise data collection and transmission. The research outlines the system's design, emphasizing the hardware components, including microcontrollers (ESP32), sensors, communication modules, and cloud platforms. Technical specifications of the DC brushed motor, covering key parameters such as voltage, frequency, speed, and power, are detailed to provide a clear operational context.
The experimental methodology involves testing the system under varied conditions to assess its performance in data collection and real-time control. The system's capability is demonstrated through continuous data acquisition from sensors, with results transmitted to the Blynk cloud platform for remote analysis and visualization. The Arduino IDE and Blynk platform are selected for their effective integration with IoT components, simplifying data handling and enhancing user interaction.
Results show that the IoT-based system provides significantly improved accuracy compared to traditional manual data collection. Quantitative analysis indicates that error metrics, including Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), reflect reduced discrepancies, validating the reliability of the IoT system. Specifically, the IoT approach improved precision by up to 15% over manual methods and enabled a 20% faster response for performance assessments and interventions.
The study concludes that IoT technology facilitates more reliable and efficient motor condition monitoring, contributing to enhanced operational efficiency in industrial applications. Recommendations for further work include expanding the system's applicability to more complex motor systems and exploring additional IoT platforms to improve scalability and robustness.