Not another alarm. A ranked answer.
Continuous monitoring that catches abnormal behaviour, sensor drift and emerging faults — with root-cause guidance for operators.
How Anomaly & Fault Detection works for your plant.
Fault detection and diagnostics (FDD) models learn what normal operation looks like across hundreds of tags, and flag deviations as they emerge. Instead of another alarm, operators get a ranked view of what changed and the most likely cause.
Inject a fault. See what moved first.
Six tags on a compressor, each inside its own alarm limits. Inject a fault: the deviation score rises before any single tag alarms, the contributing tags are ranked, and the correlated alarms collapse into one event with a likely cause.
- Score from the joint pattern, not single thresholds
- Contributors ranked per event
- Correlated alarms collapsed into one
Normal-behaviour model trained on plant history.
Live deviation scores across units.
Contributing tags ranked per event.
Root-cause guidance delivered to operators.
Fewer, better alerts.
Autoencoders learn what healthy looks like across hundreds of tags.
Every cycle gets a score; contributing tags are ranked, not just flagged.
Operators see which tags moved first and what changed.
Correlated alarms collapse into one actionable event.
“Operators do not need more alarms. They need a ranked view of what changed and why.”
From the field · SMHcoders
Will this just add more alarms?
No — the opposite. FDD replaces threshold floods with a ranked deviation view and likely causes, and nuisance alerts are reviewed out during tuning.
What data do we need to start?
Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.
How long until we see results?
A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.
Where does it run, and who sees our data?
On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.
Who owns the result?
You do. Handover includes source code, documentation, training sessions and a retraining plan.