Amygda reads the data your screening machines and baggage handling systems already produce, scores every asset by risk, and tells your team what needs attention before it stops a belt.
Acquisition data not received on 5 of 8 days, with an increasing trend.
Screening machines from one vendor. Conveyors and sortation from another. Each with its own system, its own logs, its own screen. Amygda integrates those different systems into a single source — and keeps it current as new data lands.
No new sensors, no rip-and-replace. The models read what your equipment already produces and rank every asset by how likely it is to fail.
Amygda reads logs, fault codes and maintenance history rather than tapping proprietary sensor feeds. That makes it OEM-agnostic by design — so adding a new equipment class doesn't mean starting over.
Hold baggage screening machines, plus the gantry and network they depend on — the equipment that stops bags moving when it fails.
The same approach applies to any asset with a maintenance history and a fault log. These are next, not deployed today.
Every make and model creates data and logs differently. Amygda standardises it across the airport, then hands your maintenance team the evidence, the diagnostics and the remaining useful life behind every score. So the airport keeps running at high availability — without anyone reading the millions of records, manually, your equipment writes each week.
Acquisition data not received and data chain process died logs present on 5 out of 8 days, with increasing trends.
Disable loop open log present on 5 out of 8 days, showing an increasing trend.
The actual log entries behind the score, and how many days they've been building.
Which subsystem is driving the risk, so the team knows what to look at before they open it up.
How long you've got before it fails, so the work lands in a planned window instead of a disruption.
Deployed on live airport security screening equipment, scoring risk from the data the airport already produces.
Bring a sample of your screening or BHS data and we'll show you what Amygda finds in it. Or just come and see our walkthrough!.
How reliability engineers move from reactive schedules to modelling degradation across BHS, ground support and terminal assets.
How Amygda turns fault codes, logs and maintenance records into a single ranked score per asset.
What changes operationally when maintenance teams stop reacting to failures and start acting on ranked risk.
Why an OEM-agnostic approach matters when your terminal runs equipment from several different manufacturers.
Fusing sensor data and event logs to predict failures across BHS conveyors, sortation and screening lines.
Using Remaining Useful Life predictions to cut parts consumption and hold maintenance contracts to outcomes.