Real-Time Industrial Anomaly Detection for Plant Operations
The challenge
Plants produce huge volumes of sensor data, but fixed alarm limits ignore operating modes and product mix. Teams learn to ignore alarms, subtle drift goes unnoticed, and the cause of a stoppage is rebuilt by hand afterwards.
What we built
We built a real-time anomaly detection platform that streams plant telemetry and aligns it with maintenance records and operator logs on one timeline.
Models learn normal behaviour per operating mode, score each deviation, and group related anomalies into one incident to cut alert fatigue. Each alert shows the contributing signals and recent maintenance, so operators know what changed and what to check. Engineers feed resolutions back to make it smarter.
Business value
- Less unplanned downtime
- Fewer, more meaningful alerts
- Faster investigation of incidents
- Recurring patterns tracked over time
Where else it applies
The same approach transfers to energy generation, water treatment, food production, and data centres. It belongs to our Real-Time Intelligence work: data analysed as it happens, with alerts when something needs attention, instead of reports that arrive too late.
Task
Catch abnormal plant behaviour early, with low alert noise.
Let’s build together
Got a deep-tech challenge?
Tell us the problem. We’ll show you the closest thing we have already built and how we would adapt it to your data.
Tell us what you’re working on