Platform Features · Predictive Maintenance
Hear the failure coming — 48 to 96 hours early.
ML models score every machine's health 0–100 daily from vibration, current and thermal trends. Degradation triggers a recommendation with estimated time-to-fault and the parts to pre-order.
Daily health scores 0–100Time-to-fault estimatesParts recommendationsTrend explanationsFleet rankingWhatsApp escalation
The problem it kills
Every unplanned breakdown was once a prediction nobody made.
SUDDEN
“It just failed”
Machines rarely just fail — they announce it in signatures for days. Nobody was listening. Now something always is.
CALENDAR
Time-based guessing
Calendar PMs service healthy machines and miss dying ones. Condition-based scheduling puts effort where the risk is.
SPARES
The part that isn't there
The breakdown is 4 hours; waiting for the bearing is 4 days. Time-to-fault estimates buy you the procurement window.
How it works
Predictive Maintenance, the MachineWise way.
Deployed software-first in under an hour, included in one subscription — no per-module licensing.
SCORE
Health 0–100, daily
- Fused vibration, current and thermal features
- Machine-type-specific models
- Fleet-wide ranking: worst machines first
- Score history shows degradation arcs
PREDICT
Time-to-fault, estimated
- Trending machines flagged with ETA to failure
- Likely component identified where signatures allow
- Confidence indicated honestly — no fake certainty
- 48–96h typical early-warning window
ACT
From warning to work order
- One tap: prediction → maintenance ticket
- Recommended parts listed for pre-order
- Intervention windows suggested around production
- Outcome logged to improve the model
48–96hTypical early-warning lead time
MateriallyFewer unplanned breakdowns
StrongReturn vs reactive maintenance
Keep exploring
Goes deeper with the rest of the platform.
Ready when you are
See Predictive Maintenance running on machines like yours.
A 30-minute walkthrough on a live dashboard seeded with your machine types. No obligation.
Book my free pilot → WhatsApp us