Resilient Industrial Operations through Edge AI and Digital Twin Technology for Connected Operational Environments
DOI:
https://doi.org/10.15662/IJARCST.2024.0706036Keywords:
Edge AI, Digital Twin, industrial operations, operational resilience, Industrial Internet of Things, cyber-physical systems, predictive maintenance, connected environments, real-time analytics, industrial automation, cybersecurity, intelligent manufacturingAbstract
Industrial environments are becoming increasingly connected through the integration of Internet of Things devices, cyber-physical systems, cloud platforms, industrial automation, and intelligent analytics. This transformation has created opportunities for organizations to improve productivity, predictive maintenance, resource utilization, safety, and operational decision-making. However, highly connected industrial systems also face challenges associated with network dependency, equipment failures, cybersecurity threats, data latency, and disruptions to centralized computing infrastructure. Edge Artificial Intelligence (Edge AI) and Digital Twin technology provide complementary approaches for addressing these challenges. Edge AI enables data processing and intelligent decision-making closer to industrial assets, thereby reducing latency and dependence on remote cloud services. Digital Twins create dynamic digital representations of physical equipment, processes, and production environments, enabling organizations to monitor conditions, simulate operational scenarios, and anticipate potential failures. This paper proposes a resilient industrial operations framework that combines Edge AI and Digital Twin technology within connected operational environments. The proposed approach emphasizes distributed intelligence, real-time monitoring, predictive analytics, simulation, adaptive decision-making, cybersecurity, and operational continuity. A design-oriented research methodology is proposed to examine the framework through architectural analysis, industrial use cases, performance evaluation, resilience assessment, and stakeholder-oriented analysis. The study highlights how integrating localized intelligence with continuously updated digital representations can support faster responses, improved asset reliability, reduced downtime, and more resilient industrial operations
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