| # | Machine | XGBoost risk | SVM risk | Action |
|---|
Risk = probability of a breakdown in the next 4 weeks. Rows are ranked by XGBoost. Select a machine to see its history, its top reasons, and a what-if.
Weekly telematics from 80 cranes, excavators, pumps, generators and hoists predict which machines are likely to break down in the next four weeks, so the plant team can inspect and repair them before they stop a site. XGBoost and a Support Vector Machine both run live in your browser.
| # | Machine | XGBoost risk | SVM risk | Action |
|---|
Risk = probability of a breakdown in the next 4 weeks. Rows are ranked by XGBoost. Select a machine to see its history, its top reasons, and a what-if.
Trained on Oct 2024 to Dec 2025, tuned on Jan to Mar 2026, tested once on Apr to Aug 2026. About one operating week in six is followed by a breakdown, so a random ranking scores a PR-AUC of about .
PR-AUC rewards ranking the machines that do break down above those that don't. Recall at 50% precision is the share of breakdown weeks flagged when half the alerts are real.
A breakdown counts as caught if the machine was on the list in any of the 4 weeks before it.
Move the threshold and see the result on the test months. Costs come from the workbook: a missed breakdown costs its actual repair plus replacement hire for the downtime; a caught one becomes a planned repair (35% of the repair cost plus 1 day of hire); every alert costs an inspection (Rs 6,000 plus half a day of hire).
Sudden external damage gives no warning in the sensors. No model can catch it reliably, and none should claim to.
Mean absolute SHAP value on the test months. The model relies on the signals a plant engineer would check.