An Innovative Approach for Predictive Maintenance of Home Boilers: ECOSMART Framework

One of the preeminent topics of the last decades is environmental sustainability which has attracted a huge amount of contributions also thanks to the growing interest in the so-called Green Deal initiative. Its requirements (e.g., optimizing energy consumption in all sectors) have already contributed to the enhancement and renovation of various fields, including the smart home one. In this context, one of the paradigms that is contributing the most is Predictive Maintenance (PdM). It emerges as a key strategy to offer a proactive approach to minimize energy waste and improve the longevity of home appliances. This paper proposes an innovative Adaptive ECOS-MART Boiler Management Framework that utilizes the Internet of Things (loT) and cutting-edge technologies to support the predictive maintenance of boilers in homes. Unlike the traditional boiler maintenance systems that only rely on static maintenance schedules and manual monitoring, our proposed framework introduces a Dynamic Predictive Maintenance (DPM) strategy. The proposed system employs loT sensors from real-time data, edge computing for local data processing, a cloud-based platform for advanced data analytics, and a user interface for maintenance alerts and live-time system monitoring. The paper discusses the effectiveness of the framework in reducing boiler downtime and maintenance costs and also in improving the energy efficiency of smart homes.