Automated Deep Semantic Analysis and Knowledge Graph Augmentation for Predictive Maintenance in Industrial Turbomachinery

**Abstract:** This paper presents a novel framework, *HyperScore Predictive Maintenance (HSPM)*, for enhancing predictive maintenance strategies in industrial turbomachinery. HSPM leverages a multi-layered evaluation pipeline coupled with hyperdimensional processing and reinforcement learning to automatically analyze vast and heterogeneous datasets (sensor readings, maintenance logs, operating manuals) and generate a prioritized risk assessment for each component. The framework's core innovation lies in its adaptive scoring function which quantifies the combined impact of logical consistency, novelty of failure patterns, predicted impact on production, and reproducibility of findings, leading to a significant reduction in unplanned downtime and improved operational efficiency. Implemented via a digital twin simulation, HSPM demonstrates potential for a >20% reduction in maintenance costs and a >15% increase in operational uptime across diverse turbine configurations.