Predict asset problems earlier and plan maintenance smarter.
Critical assets often show weak signals before failure. Intelecy helps reliability and maintenance teams use process data to detect developing faults, forecast risk, and plan intervention before unplanned downtime occurs.
What makes this operationally expensive today.
Reactive maintenance
Critical assets fail without warning; emergency callouts burn parts, shifts, and trust.
Weak early signals
Vibration, current, pressure, and temperature drift quietly long before alarms trip.
Unplanned downtime
Even a few hours of stoppage on a constraint unit destroys the monthly plan.
The process indicators behind every recommendation.
Intelecy reads these signals from your Historian, DCS, and SCADA — no new sensors required to get started.
From signal to action, mapped to the modules.
- STEP 01Data Explorer →
Establish per-asset healthy envelopes
Use Data Explorer to define what normal looks like for compressors, pumps, gearboxes, mills, or turbines.
- STEP 02Anomaly detection →
Detect developing faults
Multivariate residual models flag drift early — with the top contributing variables operators can investigate.
- STEP 03Forecasting →
Forecast time-to-event
Project residual trajectories so maintenance can schedule intervention in a planned window.
- STEP 04Operator workflow →
Route to the right team
Severity tiers, ownership rules, and CMMS hand-off keep reliability work where it belongs.
How customers measure success.
Reliability program, process plant
"We catch bearing degradation a week before it would have tripped the line — every time, on every machine class."
What it takes to stand this up.
20–40 tags per critical asset (vibration, current, temp, pressure, flow) from your Historian; CMMS work-order history optional.
First model live in 1 week; full fleet rollout in a quarter.
Reliability lead, maintenance planner, process engineer, plant IT/OT.
Read-only Historian / DCS access via Gateway. Alerts to email, Teams, or CMMS (e.g. SAP PM, Maximo).
See it on your own plant data.
A 30-minute working session with our process AI team. Bring a unit, a goal, and a few tags — leave with a working model proposal.