Predictive Maintenance System for Aircraft Components Using IoT Sensor Data Analytics

Objective: To develop a predictive maintenance solution that forecasts failures of critical aircraft components using real-time sensor data, reducing downtime and maintenance costs for Indian airlines.

Indian airlines face high operational costs and flight delays due to unscheduled maintenance of aircraft components, largely driven by reactive maintenance practices and insufficient use of sensor data. With increasing air traffic and a growing aviation market, efficient maintenance is crucial for safety and profitability.

The project will leverage real-time and historical IoT sensor data from aircraft (e.g., engines, landing gears) to identify patterns leading to component failure. The team will conduct domain research, preprocess sensor data, and apply machine learning models for failure prediction, referencing DGCA and airline maintenance data.

Deliverables include a data pipeline prototype, predictive analytics dashboard (e.g., Power BI), model performance evaluation, and a business case for implementation. The team will analyze Indian regulatory constraints, airline operations, and demonstrate cost-benefit impacts for a typical Indian airline.

The solution will inform maintenance scheduling decisions, improve fleet availability, and provide actionable insights for maintenance managers, thereby supporting safer and more efficient airline operations within the Indian aviation sector.