International Journal of Science, Engineering and TechnologyAuthors: Kunal S. Thakur, Abhilash A. Netake, Harsh M. Kharebind, Sanskruti S. Suryavanshi
Abstract: This paper proposes a hybrid hardware & software based intelligent monitoring system for conventional household appliances by integrating Internet of Things (IoT) architecture with data-driven fault detection and energy analysis. The system is designed using a dual-layer framework, where an ESP32 microcontroller performs real-time data acquisition, while a Raspberry Pi handles centralized processing and decision-making tasks. Key electrical and thermal parameters such as voltage, current, and temperature are continuously monitored to establish an adaptive baseline representing normal operating conditions. Any deviation from this baseline is analyzed using a machine learning based anomaly detection technique, enabling classification of appliance states into normal, warning, and critical categories. Unlike traditional threshold-based approaches, the proposed system incorporates trend aware analysis to detect gradual performance degradation and respond accordingly through user alerts or automatic power isolation using relay control. Additionally, real-time power and energy consumption are evaluated to assess appliance efficiency and identify abnormal usage patterns. Experimental validation on a household fan setup confirms effective detection of faults such as overheating, overcurrent, and voltage variations, while significantly reducing false alarms compared to conventional method.
DOI: http://doi.org/10.5281/zenodo.20787669
