Predictive maintenance is sold as an AI product; it starts as a plumbing project. The plants that succeed connect data they already generate before buying sensors they don’t yet need.
The free tier first
Drives and PLCs already measure: motor currents, torques, temperatures, operating hours, fault buffers (our flight-recorder posts cover the harvesting). Trended over weeks, these catch a remarkable share of developing failures — a conveyor motor’s slow current climb (bearing drag), a fan’s torque signature change (imbalance, fouling), thermal headroom shrinking each summer. The infrastructure is the DataLog/OPC-UA/edge patterns already covered on this blog; the analytics tier one is thresholds on trends — no machine learning required to alarm on “current at same speed and load, +15 % over baseline”.
Where dedicated sensing pays
Vibration monitoring earns its hardware on the machines where bearing failure is expensive and unpredictable: high-speed spindles, critical fans, gearboxes with lead times. Start with route-based measurements (monthly handheld rounds build the baseline culture cheaply), graduate to online sensors where criticality justifies — and let the same data platform hold both.
This staged pattern is exactly what our Zsense platform packages: machine data collection, baselines and trend alarms first; specialized sensing added per criticality — as deployed across crane and workshop machinery at Tuzla Shipyard, where predictive sensors feed centralized monitoring.
FAQ
How long before it pays? The first caught failure usually pays the pilot — realistic within the first year on a fleet with any history of surprise stops.
Machine learning ever? After baselines and thresholds have run a year and the data quality is proven — ML on unplumbed data is expensive astrology.
Zone Otomasyon deploys Zsense monitoring from pilot to plant scale — starting with the data your machines already speak. Predictive maintenance.