Auxilink Technology
Machine data and industrial IoT
Most plants already own the machines they need. What they do not have is the data those machines are producing and throwing away every shift.
We come at this from the machine side rather than the software side. Our engineers have installed, moved and repaired the equipment we are asked to connect, which decides what is practical: which signals actually exist on a fifteen-year-old machine, what can be read without touching the safety circuit, and what would need the OEM.
The starting question is never which platform. It is what decision the plant wants to make with the data, and whether the machine can produce it.
What this looks like in practice
| Machine monitoring | Cycle counts, running and idle time, and downtime reasons from machines that currently report nothing. |
|---|---|
| Utilisation visibility | What the equipment actually did, against what the plan assumed it would do. |
| Condition data | Temperature, pressure and vibration where a sensible sensor point exists. |
| Smart auxiliaries | Dryers, chillers and material handling that report their own state instead of being checked by walking to them. |
| Retrofit | Safety and controls upgrades on machines worth keeping. See automation and retrofit. |
How we would start
Not with a plant-wide programme. With a small number of machines, a defined question, and a result you can check against what you already believe about those machines.
- Pick two or three machines where downtime genuinely costs something.
- Establish what signals are actually available on them.
- Agree the one number the plant wants to see daily.
- Run it for a month and compare it against the shift reports.
- Extend only if the data changed a decision.