Predictive Maintenance for Aircraft Systems

Aerospace Predictive Analytics SQL & Tableau

Leveraging sensor and historical data to predict failures and minimize downtime for aircraft systems across 7 major ATA catalogs.

The Challenge

Aircraft maintenance data was predominantly reactive, meaning failures were only addressed after they occurred. Operational teams lacked visibility into system performance across 100+ ATA chapters, and existing reports were static and lagged by several days.

Impact: This lack of predictive visibility led to unplanned downtime and reduced operational readiness.

The Solution

I built a data-driven predictive maintenance framework to shift from reactive to proactive maintenance.

Predictive Modeling: Built models leveraging sensor and historical maintenance data across 7 major ATA catalogs.
Real-Time Monitoring: Developed real-time Tableau dashboards integrating maintenance, operational, and telemetry data.
Alerting System: Used SQL and Tableau to identify early failure indicators and generate alerts for maintenance scheduling.

The Impact

Operational Readiness: Improved aircraft readiness rates and reduced unplanned downtime across multiple systems.
Efficiency: Reduced manual report generation time by 60% through automated refresh routines.
Data-Driven Planning: Enabled maintenance teams to plan schedules based on data-driven failure predictions rather than just fixed intervals.