A Systematic Review of Predictive Maintenance and Security Co-Design with Robotic Assembly Lines: Methods, Architectures, and Future Research Directions

Abstract

Predictive maintenance (PdM) integrated with security co-design has emerged as a critical paradigm in modern robotic assembly lines, driven by Industry 4.0 and cyber–physical system advancements. These environments demand not only high operational efficiency and minimal downtime but also robust cybersecurity to safeguard interconnected systems. This review examines recent developments in the convergence of predictive maintenance and security-aware architectures. AI-driven PdM models, including machine learning and deep learning techniques, are widely used to forecast equipment failures and optimize maintenance schedules by leveraging sensor data, industrial IoT, and edge computing for real-time monitoring. Such approaches demonstrate high prediction accuracy and significantly reduce downtime in robotic systems. Simultaneously, security co-design has gained importance due to rising cyber threats targeting industrial control systems, where vulnerabilities in communication protocols and IoT integration can cause major disruptions. Integrated frameworks combining PdM with cybersecurity mechanisms—such as anomaly detection, intrusion detection systems, and blockchain-based solutions—enhance system reliability and resilience. Architecturally, hybrid models that integrate physics-based, knowledge-based, and data-driven approaches are increasingly adopted for improved robustness. Emerging trends include digital twins, explainable AI, and secure collaborative robotics, supported by edge computing for reduced latency and improved privacy. However, challenges persist in data integration, interoperability, and balancing computational efficiency with security requirements.