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Outcome — Reliability

Turn the 2am failure into a scheduled Tuesday repair.

Unplanned downtime is the most expensive way to learn an asset was sick. TwinEdge reads the data you already collect, runs it through a physics-based twin, and flags the degradation weeks before the breakdown — so the work happens on your schedule, not the failure’s.

WITHOUT TWINEDGE — REACTIVEFAILURE · 02:14emergency callout + OTWITH TWINEDGE — PREDICTED & PLANNEDearly warning · -3 wksscheduled · Tue 10:00Same pump. Same sensors. Different outcome.Catch the degradation weeks early → planned daytime repair instead of a 2am emergency.

What unplanned downtime really costs

The repair bill is the smallest line item. The real cost is everything around it — lost throughput, emergency labor, collateral damage, and the compliance exposure of an unplanned event.

Lost throughput

A critical pump or blower down means product not made, water not treated, and SLA or permit clocks running against you.

Emergency labor

After-hours callouts, overtime, expedited parts, and a crew pulled off planned work to firefight a preventable failure.

Collateral damage

A bearing that fails hard takes the seal, the coupling, and sometimes the motor with it. Caught early, it is a small part on a planned order.

How TwinEdge catches it early

From the data you already have to a decision you can defend.

01

Read the signals

Vibration, flow, pressure, current, and temperature — from your existing SCADA and historian, no new sensors required to start.

02

Compare to physics

The digital twin computes the ideal operating point and measures drift: off-BEP operation, cavitation risk, fouling, thermal rise.

03

Predict the runway

Physics-informed ML estimates remaining useful life and degradation trend, so you know not just that it is failing, but how long you have.

04

Schedule the work

A reviewed work order with diagnosis, parts, and priority lands in your CMMS — for you to approve and plan into a daytime window.

The savings, modeled

Modeled projection

Indicative figures from physics-based modeling on representative rotating equipment. Your numbers depend on asset criticality, duty cycle, and current maintenance posture — we build the model with your data during evaluation.

3–6 wks

Typical early-warning lead time

Modeled detection horizon for progressive failure modes on monitored rotating assets.

5–8×

Planned vs. emergency cost ratio

Industry-modeled cost of a scheduled repair versus the same repair done as an emergency callout.

Hours

Avoided unplanned downtime per event

Throughput preserved by converting a hard failure into planned work.

Figures are modeled, physics-based projections for illustration — not measured customer results. TwinEdge builds an asset-specific model from your historian data during evaluation so the numbers reflect your equipment.

Find your next failure before it finds you

Bring one critical asset and its historian tags. We will model its failure modes and show you the early-warning signal — on your own data.