Rotating equipment
Pumps, motors, fans, compressors and gearboxes, where vibration and temperature signatures change measurably before failure.
AI Capabilities & Services
Predictive Maintenance
Anticipate equipment failures and cut unplanned downtime with AI.
The problem
Maintenance is either too early or too late. Fixed schedules replace parts that had life left in them; run-to-failure stops the line at the worst possible moment and turns a part into a production loss, an overtime bill and a missed delivery. Most plants already have the data that separates the two — vibration, temperature, current draw, work-order history — and no practical way to use it while it still matters.
Who this is for
- Plant and maintenance managersFewer unplanned stops, and a defensible reason to move a scheduled one
- Reliability and asset engineersEarly warning with enough lead time to order the part and book the window
- Operations directorsMaintenance spend that tracks condition rather than the calendar
What people use it for
Production line availability
Ranking assets by the cost of their failure, not the probability alone, so attention goes where a stop actually hurts.
Field and distributed assets
Equipment spread across sites where a technician visit is expensive and needs to be worth making.
Spare-parts planning
Turning predicted failures into a parts forecast, so the stock held matches the risk carried.
What it needs to work
- Sensor or historian data — vibration, temperature, pressure, current, flow
- Maintenance history: work orders, failure codes, parts replaced, downtime records
- An asset register with criticality — which machines matter and why
- Connections to what you already run: SCADA or historian, CMMS or ERP, and the alerting channel your technicians actually read
How it works
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Signals in
Sensor and historian data is collected continuously, aligned in time and cleaned of the gaps and spikes that every real plant produces.
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Condition modelled
Models learn what normal looks like for each asset — not for the asset class — because two identical pumps in different duties do not behave alike.
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Deviation detected
Departures from that baseline are scored and ranked, with the contributing signals shown so an engineer can judge whether to act.
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Alert with lead time
The alert states what changed, how confident the model is, and roughly how long you have — a warning with no lead time is just a louder alarm.
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Into the work order
Confirmed alerts raise a work order in your CMMS with the evidence attached, so the loop closes in the system your team already uses.
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Learn from the outcome
What the technician found is fed back. A model nobody corrects drifts, and one that cries wolf gets ignored within a month.
How we deliver it
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Asset and data assessment
Which assets are worth modelling, what data exists for them, and how far back it goes. This is also where we say if the data will not support it.
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Pilot on a critical line
One line or asset class, run against history first, then live. A pilot that cannot be measured is a demonstration.
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Integration and alerting
Wiring into the historian, the CMMS and the channel your technicians read, with the alert thresholds tuned to what they will actually act on.
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Rollout and handover
Extending to further assets, with your team trained to read the output, retrain the models and add assets without us.
Where it runs
- On-premises, where plant data does not leave the site
- Private cloud, on your own tenancy
- Edge processing at the line, where connectivity is poor or latency matters
Security and governance
- Operational data stays inside your boundary unless you decide otherwise
- Every alert is traceable to the signals that produced it — no unexplained scores
- Model versions, retraining dates and threshold changes are recorded
- Access follows your existing roles; a technician sees their assets, not the plant
Timeline
The timeline is set by your data, not by us. Where a historian already holds a year or more of readings against recorded failures, a pilot can be scored against history in weeks. Where sensors need adding or failures were never coded consistently, the honest answer is that the first phase is instrumentation and record-keeping, and we will say so at assessment rather than after you have signed.
What you receive
- A working model per asset class, with its performance measured against your own history
- Alerting integrated into your CMMS and your team's channel
- A condition dashboard ranked by consequence, not by score
- Documented thresholds, retraining procedure and escalation path
- Training for the engineers who will own it after handover
Related work
Published projects where we did this.
What this does not do
This does not predict failures with no measurable warning — a bearing that shatters from a single impact leaves no trend to find. It does not replace inspection, and it will not work where the sensors do not exist or the maintenance history was never recorded. If your data cannot support a model, we will tell you at assessment; that is a cheaper answer than a pilot that quietly fails.
Questions we are asked
How much history do you need?
Enough to have seen the failures you want predicted, several times. A year of readings with two recorded failures teaches a model almost nothing. Where history is thin we start with anomaly detection, which needs only normal operation, and move to failure prediction as events accumulate.
What about false alarms?
They are the thing that kills these systems, so the threshold is a business decision, not a technical one: how many unnecessary checks is one avoided stop worth? We set it with you, measure it, and tune it against what your technicians actually find.
Do we need to replace our CMMS?
No. The work belongs in the system your team already opens every morning. We integrate with it rather than adding a screen nobody has a reason to visit.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
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From the floor to the boardroom
Predictive Maintenance
Anticipate equipment failures and cut unplanned downtime with AI.