Real-time digital replicas of power stations, water treatment plants, and transmission networks. Built to predict failure before it happens, not to animate a brochure.
A national water and power authority operated 47 power generation units, 12 water treatment plants, and 2,800 kilometers of combined transmission pipeline. Maintenance was scheduled by calendar: every pump every six months, every turbine annually. Some assets failed three weeks after service. Others ran for two years with no attention and no degradation.
The brief was specific: build a digital twin that predicts equipment failure from actual operating data, not from manufacturer warranty periods. The model had to cover 23 critical asset classes, integrate with the existing SCADA historian, and produce maintenance recommendations the field crews could act on.
Every model was built by engineers who had previously commissioned the same equipment types, not by software developers who had only seen them in training videos.
The result: a live digital twin platform running on 847 sensor streams, predicting bearing failures in pumps up to 14 days before seizure, and reducing unplanned outage hours by 61 percent in the first operational year.
The authority had invested in a SCADA historian that collected data from 1,200 field sensors, but the data sat in a database and was only queried after a failure had already occurred.
Three pump bearing seizures in one quarter had caused 72 hours of unplanned water supply interruption to a city of 4 million people. The maintenance logs showed all three pumps had been serviced within the manufacturer recommended interval.
We built physics-based degradation models for each asset class: vibration spectral analysis for rotating machinery, thermal cycling fatigue for heat exchangers, and cavitation erosion curves for pump impellers. Each model was calibrated against the specific operating history of the individual asset, not against generic manufacturer curves.
The models were deployed on an edge computing layer at each site, with central aggregation for cross-asset pattern detection. A pump showing the same vibration signature that preceded three previous bearing failures at other sites would trigger a maintenance order automatically.
The platform is now live across all 47 sites, processing 847 sensor streams in real time. In the first 12 months of operation, it predicted 11 bearing failures, 4 heat exchanger tube leaks, and 2 transformer insulation degradation events before they caused outage.
Unplanned outage hours dropped 61 percent. Calendar-based maintenance tasks were reduced by 38 percent, with the freed crew capacity redirected to condition-driven work orders generated by the twin.
The same five stages we run on every engagement, applied here. Hover or tap a stage to see what it covered.
Every item below was produced by engineers who had commissioned the same equipment types, for the maintenance crews who would use the twin every day.
What this engagement produced, and the evidence base behind the team that delivered it.
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