Predictive Maintenance for Engine Optimization

Situation

Aircraft engine failures can lead to costly repairs, flight delays, and operational inefficiencies. A leading legacy airline in the US sought to optimize it’s engine maintenance strategy to minimize unplanned failures, reduce downtime, and control rising maintenance costs. The goal was to transition from reactive, post-failure repairs to a predictive and preventive maintenance system.

Challenge

The airline’s maintenance division faced several hurdles in optimizing engine servicing:

• High Post-Failure Repair Costs: Reactive maintenance led to expensive, unscheduled engine overhauls.
• Lack of Preventive Triggers: No real-time mechanism to predict and prevent failures before they occurred.
• Scattered Data & Inconsistent Insights: Critical engine parameters were not systematically analyzed for early warning signals.

Solution

Mu Sigma implemented a data-driven predictive maintenance framework, integrating real-time engine health monitoring with optimization models to enhance maintenance efficiency.

1. Embedded Health Monitoring System (EHS): Developed a real-time monitoring system that continuously tracks critical engine parameters.
2. Preventive Maintenance Triggers: Created a rule-based alert mechanism to signal when preventive servicing was needed.
3. Optimization Model Implementation: Designed a Mixed-Integer Programming (MIP) model with pre-processing business rules to optimize maintenance schedules.
4. Data-Driven Decisioning: Leveraged historical failure patterns to enhance preventive maintenance accuracy.

Impact

The optimized engine maintenance strategy delivered significant efficiency gains, cutting costs and improving fleet readiness:

• 40% fewer unplanned engine failures
• 30% reduction in repair costs
• Optimized maintenance scheduling, reducing shop bottlenecks
• Proactive servicing enabled minimal operational disruptions