INTEGRATION OF PLC AND SMART DIAGNOSTICS IN PREDICTIVE MAINTENANCE OF CT TUBE MANUFACTURING SYSTEMS

Abstract

This systematic review examines the integration of programmable logic controllers (PLCs) and smart diagnostics within predictive maintenance frameworks for computed tomography (CT) tube manufacturing systems. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 87 studies were analyzed to synthesize evidence on maintenance complexity, diagnostic strategies, modeling approaches, regulatory dimensions, and economic implications. Findings highlight that CT tube manufacturing is characterized by multi-domain degradation processes, including vacuum instability, rotor-bearing wear, and thermal fatigue, which traditional preventive maintenance cannot adequately address. Recurrent failure modes—such as filament thinning, anode cracking, and vacuum leakage—were consistently associated with measurable health indicators like vibration signals, acoustic emissions, thermal gradients, and arc frequency. Prognostic modeling approaches revealed that physics-based models offer mechanistic insight, data-driven methods provide adaptive accuracy, and hybrid frameworks balance both strengths while meeting regulatory validation requirements. The review also identified unresolved challenges in interoperability, data governance, and compliance with ISO 13485, ISO 14971, IEC 60601, and FDA 21 CFR Part 11, which continue to constrain large-scale implementation. Conceptual anchors derived from the synthesis—deterministic PLC control, validated diagnostic science, and regulatory alignment—provide a structured foundation for advancing predictive maintenance in regulated medical device manufacturing.