Business Context
VoltForge Manufacturing operates 1,200 CNC machines across 8 plants and loses significant revenue from unplanned downtime. The operations team wants a predictive maintenance model that flags machines likely to fail within the next 7 days so maintenance can be scheduled proactively.
Dataset
You are given machine-level daily records collected over 24 months.
| Feature Group | Count | Examples |
|---|
| Sensor statistics | 18 | vibration_mean, motor_temp_max, pressure_std, spindle_current_p95 |
| Usage and load | 9 | runtime_hours, cycles_completed, load_factor, shift_count |
| Maintenance history | 7 | days_since_last_service, parts_replaced_30d, prior_failures_90d |
| Machine metadata | 6 | machine_type, plant_id, manufacturer, install_age_days |
| Environmental | 5 | ambient_temp, humidity, dust_index, coolant_quality |
- Size: 420K machine-days, 45 features
- Target: Binary label indicating whether the machine experiences a failure event in the next 7 days
- Class balance: 4.6% positive, 95.4% negative
- Missing data: ~12% missing in some sensor feeds due to telemetry dropouts; 6% missing in maintenance logs for older plants
Success Criteria
A good solution should achieve strong early-warning performance: PR-AUC above 0.45, recall above 75% at precision above 35%, and provide interpretable drivers so plant engineers can trust the alerts.
Constraints
- Predictions run as a daily batch job before the morning maintenance planning meeting
- Scoring all machines must complete in under 10 minutes
- The solution must be reasonably interpretable for reliability engineers
- False positives are acceptable up to a point, but excessive alerts will overwhelm maintenance crews
Deliverables
- Build a binary classification model to predict failures within 7 days.
- Explain model choice, preprocessing, and how you handle missing data and class imbalance.
- Create useful engineered features from sensor, usage, and maintenance history.
- Evaluate the model using metrics appropriate for imbalanced classification.
- Recommend a decision threshold and describe how the model would be deployed and monitored in production.
