Predictive Maintenance
AI watches your machine signals, vibration, temperature and run hours, spots the early signs of wear, and flags which machine needs attention and roughly when.
Why It Matters
An unplanned stop drags in overtime, a rushed spare part and a line that sits idle while everyone waits. When AI flags the machine days ahead, you plan the repair into a shift when it hurts least and keep the order on time.
Vibration creeping up, temperature climbing, run hours stacking, small changes that a person cannot track across a full floor. AI reads these signals alongside your maintenance history and ERP records, and learns the pattern that comes before a failure.
The service pulls in vibration, temperature, load and run hours from your machines and sensors, and joins them with maintenance history and ERP records like work orders and downtime. It learns each machine's normal range instead of using a generic threshold.
Instead of a single alarm, the model gives a health reading per machine and an estimate of how long you have before it is likely to fail. That lead time is what lets you plan the repair rather than scramble when the line stops.
Each prediction turns into a maintenance task your team can slot into a shift that suits the plan. The warning lands where your maintenance team already works, so it drives action instead of sitting in yet another tool nobody checks.
Rising vibration and heat that point to a bearing or gear heading toward failure, days before it seizes.
Temperature creeping above the normal range for a motor, spindle or drive, so you act before it trips or burns out.
Motor load and current drifting in a way that signals a struggling drive or a developing electrical fault.
Pressure and flow readings that fall out of range, hinting at a blocked line, a failing pump or low lubrication.
A machine quietly taking longer per cycle, an early sign of wear that shows up before any alarm.
A rise in reject or rework rate tied to a specific machine, linking product quality back to machine condition.
We pull in sensor, machine and ERP data, plus your maintenance history.
The model learns each machine's normal running range and past failures.
It tracks every signal in the background and looks for early drift.
It flags which machine needs attention and roughly when.
The warning becomes a work order planned into the right shift.
Unplanned breakdowns
Downtime and idle labour
Repairs, not emergencies
Life from each machine
Missed and late orders
Emergency repair spend
We build ERP and software, so we tie machine signals to your work orders, downtime and stock data, not just a dashboard.
We start with the signals your machines already produce, so you do not have to rip and replace to get going.
We pick the AI approach that fits your machines and data, trained on your own history, with no vendor lock-in.
Warnings land as clear work orders your maintenance team can act on, not raw alerts nobody reads.
We start with the machines where a failure hurts most, prove the value, then widen from there.
Predictive maintenance uses AI to watch machine signals such as vibration, temperature and run hours, spot the early signs of wear, and flag which machine needs attention and roughly when. You fix things on a plan instead of waiting for a breakdown.
It works from the machine and sensor data you already collect, such as vibration, temperature, load and run hours, combined with maintenance history and ERP data like work orders and downtime records. Where a machine has no sensors, simple readings or manual logs can still be used to start.
Scheduled maintenance services every machine on a fixed calendar, whether it needs it or not. Predictive maintenance watches the actual condition of each machine and flags the one that is heading toward failure, so you act on the machine that needs it at the right time.
No. We start with the signals your machines already produce and the data in your systems. If a critical machine has no monitoring at all, we can advise on a small set of sensors, but the aim is to use what you have first.
We are model-agnostic. We pick the approach that fits your machines and data rather than tying you to one vendor, and the models are trained and tuned on your own history.
Once we have enough machine history and a few failure examples to learn from, early warnings can start within a few weeks. Accuracy improves as the model sees more of your machines running over time.
Related
The other services in this section, and related pages from TCB Infotech.
No commitment. A practical first look at predictive maintenance for your machines.
