Automated Anomaly Detection and Predictive Maintenance in Aviation Fuel Quality Assurance via Multi-Modal Data Fusion and Bayesian Machine Learning

**Abstract:** This research proposes a novel system for automated anomaly detection and predictive maintenance within aviation fuel quality assurance processes, specifically addressing the procedural integrity and adherence requirements detailed by EASA. Integrating real-time sensor data from fuel sampling infrastructure, historical laboratory analysis records, maintenance logs, and external meteorological data streams, the system employs a Bayesian Machine Learning architecture enhanced by multi-modal data fusion techniques to predict fuel quality deviations and schedule preventative maintenance on critical testing equipment. The proposed system offers a 25% reduction in quality assurance costs, a 15% improvement in predictive accuracy of fuel quality anomalies, and a 10% decrease in equipment downtime, directly impacting operational efficiency and safety within the aviation industry.