Predictive maintenance with IoT
Short answer
Predictive maintenance uses IoT sensors (vibration, temperature, current, acoustic) and models that learn normal patterns to anticipate failures. It replaces preventive (calendar) and corrective (post-failure). On motors, compressors, and pumps, typical ROI hits at 12-24 months by cutting unplanned downtime 30-50%.
What to measure per asset
Electric motors: vibration, current, temperature. Compressors: pressure, temperature, power. Pumps: flow, vibration, current. Belts: speed, bearing temperature. No need to measure everything — 2-3 well-chosen signals usually suffice.
From data to useful alert
Chain: sensor → edge gateway → platform → ML model → CMMS (Maximo, SAP PM, etc.). Key is that alerts reach the tech with a concrete action (what to replace, when, which part), not just an alarm.
ROI: how to measure it honestly
Compare before and after: unplanned downtime hours, corrective maintenance cost, asset life. A serious pilot establishes baseline before touching anything.
- Asset-specific sensors
- Edge gateway with pre-processing
- ML on cloud or edge
- CMMS integration
- Baseline + KPIs before deciding
Free maintenance audit
We review your critical assets and propose a measurable 90-day pilot with estimated ROI.
Frequently asked questions
Do I need in-house data scientists?+
For pilots with vertical solutions (Augury, Uptime, OEM platforms) no. For plant-wide expansion with in-house models, yes — a data + OT team helps.
What about old assets with no connectivity?+
Retrofit with external wireless sensors (LoRaWAN or cellular NB-IoT/LTE-M). That's 80% of real cases: nobody replaces motors to digitize.
ROI in how long?+
12-24 months with a well-chosen case. Under 12 months is usually optimism; over 24, the case wasn't the right one.
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