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By: Supriya Nagarkar and Padma Mishra.
Assistant Professor, Department of Computer Science, Tilak Maharashtra Vidyapeeth (TMV), Pune, Maharashtra, India
Associate Professor, Department of MCA, Thakur Institute of Management Studies, Carrier Development & Research (TIMSCDR), Mumbai, Maharashtra, India
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed framework employs machine learning and deep learning methods – including Random Forests, Autoencoders, and Long Short-Term Memory (LSTM) networks – to analyze multi-sensor data, detect anomalies, and predict failures before they occur. A simulated smart home environment demonstrated over 90% fault-prediction accuracy, leading to measurable reductions in energy consumption and maintenance costs. Ethical considerations such as data privacy, transparency, and algorithmic fairness are explored. Findings indicate that AI-driven PdM significantly enhances operational continuity, sustainability, and user comfort, paving the way toward autonomous, self-healing smart homes. Future research directions include federated learning, digital twins, edge AI, uncertainty-aware models, and adaptive self-learning systems.
Citation:
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