Anomaly and Fault Detection — SMHcoders
INDUSTRIAL AI // FDD-06

Anomaly and Fault Detection

Continuous monitoring that catches abnormal behavior, sensor drift and emerging faults — with root-cause guidance for operators.

FDD // EVENT LOG
02:14K-101 DISCHARGE TEMP · DEVIATION 3.2σALERT
02:14TOP CONTRIBUTORS · TI-204 · PI-101RANKED
02:16OPERATOR ACKNOWLEDGED · WO DRAFTEDLOGGED
02:40P-305 SEAL FLUSH · NORMAL BANDOK
03:05ALARM FLOOD SUPPRESSED · 14 → 1FILTERED
// What it is

How Anomaly and 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.

LIVE MONITORING // UNIT-07
TI-204OK
PI-101OK
VI-208DEVIATION
FI-330OK
LIKELY CAUSE — BEARING WEAR VI-208
// How it works

From data to running system

01
LearnNormal-behavior model trained on plant history.
02
MonitorLive deviation scores across units.
03
DiagnoseContributing tags ranked per event.
04
GuideRoot-cause guidance delivered to operators.
// Detection modes

Fewer, better alerts

FDD // MODES
Normal-behaviour modelsAutoencoders learn what healthy looks like across hundreds of tags.
Deviation scoringEvery cycle gets a score; contributing tags are ranked, not just flagged.
Root-cause guidanceOperators see which tags moved first and what changed.
Alarm-flood suppressionCorrelated alarms collapse into one actionable event.
// Industries

Where it applies

Oil and GasRenewable EnergyPowerWater
// Benefits

What it changes

FDD-06 // IMPACT BOARD
Faults caught earlierOK Fewer nuisance alarmsOK Sensor drift identifiedOK Guided operator responseOK
FROM THE FIELD

Operators do not need more alarms. They need a ranked view of what changed and why.

Book a free consultation
// FAQ

Common questions

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.