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Predictive Analytics
Built automated ETL pipeline aggregating minute-level sensor data into daily health indicators with degradation-aware feature engineering (thermal drift, vibration escalation, RPM variability). Benchmarked multiple models (Random Forest, LightGBM, XGBoost) using time-aware cross-validation, achieving 0.87 recall with XGBoostβa 22% improvement over baseline. Daily batch inference currently running in production.
System Architecture & Implementation
- Automated ETL pipeline processing 1.4M+ sensor readings daily from industrial machinery
- Feature engineering capturing degradation patterns: thermal drift, vibration escalation, RPM variability
- Time-aware cross-validation preventing data leakage with proper temporal splits
- Model benchmarking: Random Forest, LightGBM, XGBoost evaluated on business metrics
- Production deployment with daily batch inference and monitoring dashboards
- Alert system integrated with maintenance scheduling software
Technical Approach
- PostgreSQL database optimization for time-series sensor data queries
- Custom feature transformations for degradation pattern detection
- Hyperparameter tuning using Optuna with business-focused objective function
- Class imbalance handling with SMOTE and class weights
- Model interpretability using SHAP values for maintenance team insights
Tech Stack
Python 3.10
XGBoost
LightGBM
Pandas
NumPy
Scikit-learn
PostgreSQL
SQLAlchemy
Optuna
SHAP
Joblib
Results & Business Impact
- Achieved 0.87 recall (22% improvement over baseline Random Forest at 0.71)
- Reduced unplanned downtime by predicting failures 3-7 days in advance
- Enabled shift from reactive to predictive maintenance strategy
- System processing 50+ machines with 99.2% uptime
- Cost savings estimated at $200K+ annually through optimized maintenance scheduling