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Reactive
You go to the machine after it has stopped. You pay for the lost output, for the part bought at the last minute and, often, for the extra damage done by a machine that kept running faulty.
The starting pointPredictive maintenance
A machine gives signs before it breaks. The models read them.
Preventive maintenance runs on fixed rules — so many days or so many hours. That is far better than repairing after the machine has stopped, but it treats two machines working in completely different conditions the same way. The predictive part adds a third rung: how often a service happens is fitted to how each machine behaves.
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Three rungs
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Preventive
You go to the machine when the service comes due, counted in days or in hours actually run. Unexpected stoppages drop, and the job is placed when stopping costs least.
Available from day one - 03
Predictive
How often a service happens is fitted to the state observed: the readings off the machine, how long each job took, the measurements from the procedures and the parts used. Machines that start behaving differently from usual are flagged before they break.
As history builds up
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What data the models work on
The models do not guess. Every source below goes into the platform anyway, out of the day-to-day work.
Operating hours and cycles
Counted per machine, from the production activity or straight off the machine.
Measurements from procedures
The values read at each service, together with the limits written in the procedure. That gives a series over time for every parameter.
Readings off the machine
What connected machines send in: temperatures, pressures, vibration, consumption, and whether they are running or standing.
Job history
What was done, when, how long it took against what was expected, and which parts went in.
Reports and breakdowns
The faults reported by people and the unplanned jobs, with a description and photos.
Parts consumption
Which parts were replaced on each machine and at what interval. It is the most direct sign of how fast something is wearing.
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What the model gives you
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A service date fitted to the machine
When to do the next service on that machine, fitted to how it works, not to a general scale.
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A signal when something leaves the pattern
Machines that start behaving differently from usual are marked, along with the parameter that gave them away.
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The parts to buy
Which parts and materials you will need in the coming period, added up across the whole fleet, so you can buy to plan.
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What you will spend
The maintenance spend estimated over the period you choose, per machine and in total.
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How data is handled
- The models are not called directly. A hub of our own sits between the platform and the model providers.
- The hub keeps no prompts, no responses, no vectors and no audio recordings. It has nowhere to put them: the database schema has no fields for them, so this is not a setting anyone can change.
- Your operating data stays with you, in your own installation. Only what the current question needs goes to the model.
- Every recommendation comes with the data it leaned on, so the maintenance manager can check it before acting.