Dew computing with edge intelligence for industrial automation and predictive maintenance real-time anomaly detection

The increasing complexity of industrial automation systems, coupled with the pressing demand for realtime decision support, necessitates the deployment of efficient and decentralized computing paradigms. Edge computing (EC), operating at the periphery of the network, offers significant advantages by enabling localized data processing and reducing reliance on centralized cloud infrastructures. Building on this concept, this paper introduces a novel framework that integrates edge intelligence with dew computing (DC) to advance industrial automation and predictive maintenance. The proposed approach employs lightweight algorithms for real-time anomaly detection at dew nodes, enabling early identification of operational deviations in industrial equipment while maintaining minimal resource usage. Furthermore, causal inference models are incorporated to determine the root causes of equipment failures directly within the dew layer, thereby enhancing the precision of maintenance strategies and minimizing downtime. By leveraging localized computation, the framework effectively reduces latency, optimizes energy consumption, and enhances system reliability. Experimental evaluation demonstrates that the system achieves 96.3% accuracy in anomaly detection, correctly identifies root causes in 92.7% of cases, reduces average latency to 10.6 ms, and consumes only 2.4 W of power per dew node. A case study conducted in a smart manufacturing environment validates the practical benefits of the framework, highlighting improvements in anomaly detection and maintenance scheduling. The study also examines scalability and energy efficiency, underscoring the potential of the proposed system for deployment across diverse industrial settings.