PREDICTIVE MAINTENANCE
Most maintenance is scheduled on a calendar the machine never agreed to
Fixed-interval servicing replaces parts that still have life in them and misses the ones about to fail. The information that would tell you the difference is usually already being generated — bearing vibration, motor current, drive temperature, line speed — and usually thrown away seconds after it is read. Predictive maintenance is less about exotic algorithms than about capturing those signals continuously, at a resolution that makes degradation visible, and relating them to what the machine was actually doing at the time.
What the system does
Signals from the equipment itself
We read from the PLC and drive layer first — motor current, torque, speed, temperature, cycle timing — because those exist already. Dedicated vibration or acoustic sensors get added where the failure mode genuinely needs them, not as a default.
Context, not just raw signal
A current trace means nothing without knowing the product, the speed setpoint and the operator action at that moment. Because we build the MES layer, the condition data arrives already tied to the production context that explains it.
Baselines per machine, not per fleet
Two nominally identical machines have different signatures after a decade on the floor. Models are fitted against each asset’s own history, so an alert means “this machine has changed”, not “this machine differs from the catalogue”.
Failure modes defined with your maintenance team
We start from the handful of failures that actually hurt you — the ones your technicians can name — and work backwards to what would indicate them. That beats an unsupervised model flagging anomalies nobody can act on.
Alerts that reach a person with a job to do
A prediction that lands in a dashboard nobody opens is worthless. Alerts route to the maintenance planner with the asset, the evidence trace and the suggested window, and they are logged so the prediction can be scored later.
Honest confidence
Every prediction carries a confidence and the data behind it. We would rather tell you a model is not yet reliable for a given asset than have your team learn to ignore the alerts.
How a predictive maintenance project runs
Failure review
A working session with maintenance and production: which failures stop the line, how often, how they are currently caught, what they cost in lost output. This narrows the problem before any technology decision.
Data layer
We instrument and historise the relevant signals at the resolution the failure mode needs. This phase is real engineering work and we are explicit about it — it is also the part most AI projects skip and then fail on.
Observation period
Models need to see degradation, not just healthy running. Depending on failure frequency this is weeks to months. During it you already get condition dashboards and trend alarms, so the period is not dead time.
Model, deploy, score
Models go live alongside the existing maintenance process, not instead of it. Every prediction is scored against what the technicians actually found, and that feedback is what makes the second year materially better than the first.
What this is built on
Signal acquisition through OPC UA and native drivers for Siemens S7 and LSIS PLCs, with Windows collection services writing to a time-series store sized for the sampling rate. Modelling in Python — gradient-boosted models and survival analysis for remaining-useful-life, with spectral methods where vibration is involved. Everything runs on your infrastructure or in an EU data centre of your choosing; plant data does not leave the environment you approve.
Start with the failures that actually cost you
Tell us the two or three failures that stop your line most often. We will tell you honestly whether the signals to predict them are likely to exist, what it would take to capture them, and whether the economics work.