International Journal of Science, Engineering and TechnologyAuthors: Rohan Mehta, Arvind Sethi, Nisha Kulkarni, Vasudev Sharma
Abstract: The increasing dependence on connected sensing infrastructures in healthcare and industrial environments has intensified the need for predictive reliability and maintenance strategies capable of operating under stringent latency, safety, and availability constraints. Conventional cloud centric analytics architectures often struggle to meet these demands due to communication delays, bandwidth limitations, and reduced resilience during network disruptions. This study argues that integrating edge level artificial intelligence with multi modal sensor fusion offers a structurally superior approach for anticipating failures and sustaining reliable operation in cyber physical systems. The paper proposes a decentralized analytical framework in which heterogeneous sensor streams are processed locally using lightweight machine learning models deployed at the network edge, enabling real time condition assessment and predictive maintenance decisions without persistent cloud dependence. Drawing on established principles from reliability engineering, signal processing, and embedded intelligence, the framework is examined across two high impact domains healthcare Internet of Things systems and industrial monitoring infrastructures. Conceptual experiments and comparative analyses demonstrate that edge driven sensor fusion improves fault detection sensitivity, reduces maintenance response latency, and enhances system robustness when compared to centralized predictive maintenance pipelines. Empirical patterns suggest that early anomaly recognition at the edge significantly mitigates cascading failures in safety critical devices, particularly in environments characterized by continuous operation and constrained connectivity. Beyond performance gains, the findings highlight important implications for system governance, data integrity, and operational autonomy. By advancing a unified edge intelligence paradigm applicable across domain boundaries, this study contributes a scalable and resilient foundation for next generation predictive reliability and maintenance analytics, positioning edge AI as a critical enabler of trustworthy and sustainable cyber physical systems.
