A Big Data Analytics Architecture for Predictive Maintenance and Risk Mitigation in Cyber-Physical Systems
Keywords:
cyber-physical systems, predictive maintenance, risk assessment, big data analytics, machine learning, edge computing, cloud infrastructure, fault detection, industrial IoT, operational efficiencyAbstract
The increasing integration of Cyber-Physical Systems (CPS) into industrial operations has amplified the need for intelligent, data-driven frameworks capable of supporting predictive maintenance and comprehensive risk assessment. Advances in Big Data Analytics (BDA) have enabled the processing of high-volume, real-time data streams generated by CPS, facilitating proactive responses to emerging system faults and operational risks. This paper introduces a multi-layered architecture that fuses BDA technologies with CPS environments to enhance maintenance planning and reduce system vulnerabilities. A thorough literature review outlines the evolution of predictive maintenance and risk analytics, laying the groundwork for the proposed framework. The architecture leverages edge analytics, machine learning algorithms, and cloud-based processing to enable efficient system monitoring and decision support. Visual diagrams and tabular data illustrate the architecture’s structure and applicability within contemporary industrial systems.
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Copyright (c) 2024 Kingsly Antony (Author)
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