🎯 Predictive Maintenance for Military Vehicle Fleet

AI-Powered Predictive Maintenance System - Predicting Remaining Useful Life (RUL) of military vehicles using machine learning to optimize maintenance schedules and enhance operational readiness.

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🚀 Features

🔧 Core Capabilities

• RUL Prediction: Accurate Remaining Useful Life estimation using sensor data
• Real-time Monitoring: Continuous vehicle health monitoring
• Failure Forecasting: Early detection of potential component failures
• Maintenance Optimization: Data-driven maintenance scheduling

📊 Data Processing

• Sensor Data Integration: Process multiple data streams from vehicle sensors
• Feature Engineering: Advanced feature extraction from time-series data
• Data Validation: Automated data quality checks and preprocessing
• Anomaly Detection: Identify unusual patterns in sensor readings

🤖 Machine Learning

• Multiple Algorithms: Ensemble methods, neural networks, and time-series forecasting
• Model Explainability: SHAP analysis and feature importance
• Continuous Learning: Model retraining with new data
• Performance Monitoring: Track model drift and accuracy over time

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📈 Project Demo

RUL Prediction Results

Remaining Useful Life predictions vs actual values

Feature Importance

Most influential features in predicting vehicle failures

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🏗️ Architecture

graph TD
    A[Vehicle Sensors] --> B[Data Collection]
    B --> C[Data Preprocessing]
    C --> D[Feature Engineering]
    D --> E[ML Model Training]
    E --> F[RUL Prediction]
    F --> G[Maintenance Alerts]
    G --> H[Dashboard Visualization]

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