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Use case · Predictive Maintenance

Fix machines before they fail.

Inducloud learns how each machine normally behaves, trends wear signals like vibration and temperature, and tells you when a component will reach its limit. Service it in a planned stop, in your own plant or across your customers' installed base.

The problem

The answer was in the data. Nobody connected it.

Maintenance runs on the calendar or on breakdowns. Parts get replaced too early, or too late, and the line stops when it suits nobody.

The early warning was in the machine data all along: vibration creeping up, a temperature drifting, cycles getting slower. Nobody was watching the trend.

How it works

From signal to action, step by step.

[01]

Connect the machines, any make or age.

[02]

Learn each component's normal behaviour.

[03]

Trend wear signals: vibration, temperature, pressure.

[04]

Predict when the alarm limit will be reached.

[05]

Plan service in a planned stop, not a breakdown.

[06]

Machine builders: run it across the whole installed base.

What you get

Measurable effect on the floor and in the P&L.

Fewer breakdownsLonger component lifePlanned, not panic, serviceFewer site visitsRecurring service revenue for OEMs
From assumptions to measurements
Next step

Stop maintaining by the calendar. Start with one machine.

Then we scale on proof, not promises.

Results in 4–8 weeks