The intersection of Big Data Analytics and Industrial Internet of Things (IIoT) introduces a transformative potential to improve operational efficiency, predictive maintenance, and decision-making in smart industrial systems. But such methods rarely deployed in practice, are not interoperable, and are not scalable. This study presents a resilient real-time analytics framework that combines edge-cloud computing, digital twins and secure data governance. Through validation with industrial data and benchmarking of performance the study fills the important gaps in empirical validation, legacy system integration, and fault tolerance that allow for actionable insights in a range of industrial sectors.
C. Madana Kumar Reddy, Smita Gambhire, Manoj Kumar et al.· 0 citations
This paper describes a novel AI-driven predictive maintenance approach in the context of industrial systems that overcomes the limitations of the existing approaches including high dependency on the availability of data, model complexity and generalisation. With the integration of transformer-based structures, hybrid learning, edge AI optimization and adaptive transfer learning, the proposed system improves fault prediction accuracy, reduces latency and enables real-time deployment. The integration with digital twin and federated learning also makes scalability and data privacy guaranteed. Experimental results reveal 23% increased accuracy in detecting faults and 30% decreased unplanned downtimes, which demonstrate the robustness in adaptation across a wide range of industrial environments.
Angad Singh Ojha, P. Sumathi, Manoj Kumar et al.· 0 citations
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