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Case study · Data & Analytics
A utility turned sensor streams into live views and early warnings for field crews.
The challenge
The utility had sensors on pumps, transformers and meters across a wide area. The readings were collected overnight and checked in batches. By the time an analyst noticed a pattern, the equipment had often already failed.
Field crews spent much of their day on routine inspections that found nothing. Meanwhile, real faults in remote sites went unseen. The data was large, noisy and arrived from devices of different ages, so simply storing it faster would not be enough.
Our approach
We began with crew leads and asked which decisions hurt most today. That led to a short list of equipment types and fault patterns worth watching first.
We planned how readings travel from devices to a live store, how bad readings are filtered out and how history is kept for training models.
We built the pipeline and trained simple prediction models on past failures. We tested them against old events and tuned alerts to avoid false alarms.
We launched with a small group of crews, collected their feedback on alerts and screens, and widened the rollout once they trusted the warnings.
What we built
Readings flow from field devices into a live store within moments of being taken.
A map and status board show equipment health across every site.
Models flag equipment that is drifting toward failure so crews can plan a visit.
Alerts reach mobile devices with the site, the likely issue and the history.
What changed
Crews now hear about drifting equipment before it fails and causes an outage.
Visits are planned from equipment health rather than a fixed calendar.
Operations staff can see distant equipment as clearly as the sites near the depot.
Technology
Keep reading
Plants and finance finally share one set of numbers
Crews hear about faults before customers do
Orders reach dispatch without re-keying
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