MACHINE MANAGEMENT
Most factories know their output. Far fewer know why it was not higher. Gate-IoT builds machine monitoring that records machine state, uptime, and downtime reasons automatically — including on older machines that were never designed to be connected.
The gap is rarely the machines. It is that nothing records what they did.
A machine stops, someone fixes it, and no record survives. The same fault repeats next month with no evidence it ever happened.
Availability, performance, and quality get calculated from memory and spreadsheets rather than from what the machine reported.
Supervisors spend the first hour of each shift assembling numbers that the equipment could have produced automatically.
The newest line is monitored; the machines that actually constrain output are twenty years old and report nothing.
This is what the record usually looks like before monitoring: a log with gaps, and a shift nobody can reconstruct afterwards.
Captured from the machine itself and timestamped as it happens — not entered by an operator afterwards.
Running, idle, setup, maintenance, or off — with the duration of every state change recorded automatically.
Availability, performance, and quality measured from real machine data, per shift, per machine, or per line.
Stoppages categorised so the recurring causes become visible instead of being absorbed into a single idle figure.
Parts produced against target for the current shift, with the gap visible while there is still time to act.
Machine alarms captured with their message and timestamp, and routed to whoever needs to respond.
Vibration and temperature on motors, spindles, and pumps, giving warning before a stoppage becomes a breakdown.
Which work order and part number a machine was running when the data was recorded.
Consumption tied to the machine that used it, so cost can be attributed rather than averaged.
Views are configured around how your plant runs — the examples above show the standard breakdowns we set up.
This is the part that decides whether a monitoring project succeeds. Modern equipment is straightforward. Everything else is where integration work actually earns its keep.
Where a PLC or CNC already exposes data, we read it directly over the protocol it already speaks — no change to the machine programme and no interruption to production.
Modbus TCP/RTU · OPC UA · EtherNet/IP · Profinet · Siemens · Mitsubishi · Omron · Delta
Older equipment gets retrofitted with external sensing instead of being replaced: current sensing on the motor, signal-tower detection, proximity or cycle counting, and vibration for condition. The machine keeps running exactly as it did.
Current sensing · Signal tower · Proximity & cycle count · Vibration · Temperature
Dashboards for the shop floor, for supervisors, and for management — each showing what that audience acts on.
A wall screen showing live machine status and progress against target, readable across the floor at a glance.
Current shift detail: which machines are down, for how long, and why — while the shift can still be recovered.
Trends by machine, line, shift, or product, scheduled by email or exported to Excel and BI tools.
Notifications when a machine stops, an alarm fires, or a threshold is crossed, sent to the people responsible.
We build on established IoT platforms rather than a closed product of our own, so the system stays yours and can be extended by anyone.
Monitoring tells you what happened. Once enough history exists, the same data answers a more useful question: what is about to happen.
Full-resolution machine history is retained rather than averaged away, so a model can later be trained on questions nobody thought to ask at the time.
What normal looks like for each machine is learned from its own history, because a healthy spindle in one plant is not healthy in another.
Vibration, temperature, current, and cycle time are watched for drift away from that baseline — long before any fixed alarm threshold trips.
Where a trend is consistent, it is extrapolated to estimate when a component will cross its limit, turning a breakdown into a planned stop.
Prediction is not magic, and we will not pretend otherwise. It needs a few months of clean history, a failure mode that actually trends — bearing wear, tool wear, filter clogging, belt tension — and enough recorded failures to check the model against.
We start by collecting and storing the data properly. Forecasting is added once the history can support it, so you are not paying for a model that has nothing to learn from.
Start with a handful of machines, prove the numbers, then scale.
We check what each machine can already report and what needs sensing added, then tell you what is realistic.
A small set goes live first so you can confirm the data matches reality before committing to the whole floor.
Remaining machines are connected and the dashboards are shaped around how your team actually runs a shift.
Once downtime reasons are visible, we help you use them — and extend the system as your needs change.
Tell us what you run and how old it is. We will tell you what can be monitored, what needs retrofitting, and what it costs per machine.
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