AI-enhanced predictive maintenance systems for critical infrastructure: Cloud-native architectures approach

Abstract

Critical infrastructure (CI), such as power grids, transportation systems, and telecommunications networks, is becoming increasingly complex, requiring sophisticated maintenance strategies and procedures to guarantee optimal performance and system durability. This paper examines the transformational potential of AI-driven predictive maintenance systems, highlighting their ability to prevent system failures, minimize downtime, and enhance resource efficiency. Integrating machine learning algorithms with real-time data analytics allows predictive maintenance frameworks to accurately foresee equipment failures, facilitating timely interventions that reduce the risk of catastrophic infrastructure breakdowns. This study primarily examines the development of cloud-native architectures, which include containers, microservices, and orchestration tools like Kubernetes, to facilitate the scalability, flexibility, and resilience required for contemporary CI maintenance systems. These designs facilitate the seamless integration of predictive maintenance solutions across geographically dispersed infrastructure, enabling effective administration of extensive datasets produced by Internet of Things (IoT) sensors, operational logs, and edge computing nodes. The document examines the essential function of intelligent data orchestration in facilitating the prompt gathering, processing, and analysis of operational data, which is vital for AI models to provide precise predictions. The amalgamation of AI-driven predictive maintenance with 5G and forthcoming 6G networks is poised to transform real-time system monitoring, diminishing latency and enhancing decision-making efficacy. Utilizing AI and cloud-native technologies substantially enhances system reliability, cost-effectiveness, and comprehensive infrastructure optimization. This article thoroughly analyses how AI, cloud-native platforms, and intelligent data orchestration may be utilized to tackle the changing maintenance issues of critical infrastructure by examining real-world case studies from sectors like power grids, telecommunications, and transportation. Integrating AI, cloud computing, and IoT in predictive maintenance improves system reliability and prepares critical infrastructure for future autonomous management and optimization developments. The study finishes by discussing new trends, such as the integration of digital twins and the synergies between AI and cloud-native solutions, which will enhance predictive maintenance capabilities.