XGBoost Engine // NASA C-MAPSS Telemetry

Aero Guard

Predictive Machine Learning & Jet Engine RUL Analytics

DOMAINAerospace & Predictive Data Science

ROLELead Data Scientist & ML Architect

YEAR2026

CASE STUDY IDPROJECT_01

Project Overview

Aero Guard is a predictive maintenance platform built to forecast the Remaining Useful Life (RUL) of commercial aircraft jet engines prior to structural degradation.

PythonXGBoostPredictive AnalyticsMachine LearningScikit-LearnFeature Engineering

// THE CHALLENGE & PROBLEM SPACE

Unscheduled engine maintenance in commercial aviation leads to severe flight cancellations, logistics bottlenecks, and extreme financial penalties. Traditional threshold alerts trigger after degradation starts. The challenge was building a predictive ML engine capable of modeling non-linear thermal and pressure sensor degradation trajectories over hundreds of flight cycles.

// SYSTEM ARCHITECTURE & METHODOLOGY

Engineered rolling statistical aggregates (rolling mean, rolling std, exponential moving averages) across 21 distinct engine sensors over multi-horizon window sizes. Used NASA's C-MAPSS simulation dataset to train and tune an optimized XGBoost gradient boosting regressor.

Validation Metrics & Key Deliverables

Target MetricRUL (Flight Cycles)

Model VariantOptimized XGBoost

Validation RMSE11.42 Cycles

R² Score0.865

KEY TECHNICAL HIGHLIGHTS

Extracted non-linear degradation features from 21 multi-sensor telemetry channels.

Achieved an outstanding Validation RMSE of 11.42 cycles across test turbine fleets.

Formulated early warning thresholds allowing proactive maintenance scheduling before component failure.