Enzwa lab
AugurFailure Prediction & Avoided Downtime
Condition-Based Reliability · Critical Facilities

Stop maintaining to the calendar.
Start maintaining to the signal.

Every asset whispers before it fails. This engine reads the whisper — turning sensor telemetry into lead time, and lead time into avoided downtime, lower cost and a defensible operating model for the data centre floor.

40–60%Less unplanned downtime
$100k+Per outage avoided
6–12 moTypical payback
Why this, why a data centre, why now

Margins for error are collapsing

Coupled, unforgiving systems

UPS strings, chillers, CRAC units, generators and PDUs run as one interdependent chain. A single battery, breaker or compressor can cascade into a full thermal or power event. No asset fails in isolation.

The cost of dark time

An unplanned outage averages six figures before SLA penalties, breached uptime tiers and reputational cost. Reactive, run-to-failure work is the most expensive way to discover a problem. Downtime is the product you can't sell back.

Density is rising under you

AI and advanced-semiconductor compute push rack densities and thermal loads past what the building was designed for. Power and cooling assets age faster, with tighter margins. Yesterday's PM schedule no longer fits today's load.

The Engine
Instrumented estate70%

Asset lifecycle — UPS battery string

Health from defect onset to functional failure · the P–F interval
Monitoring
day 0

Facility — live asset health

Five subsystems on the floor · click to inspect a lifecycle

This strategy vs. doing nothing

Current approach measured against a reactive, run-to-failure baseline
Reactive baseline Selected strategy

What the engine hands the FM

Auto-ranked work orders — criticality × imminence, with the lead time each buys
Model assumptions & where to plug in real data Subsystem failure rates, P–F intervals and downtime costs are illustrative defaults for a mid-size enterprise data hall. Detection lead time scales with sensor coverage and each asset's signal strength; cost and downtime reductions are anchored to published predictive-maintenance benchmarks (≈40–60% unplanned-downtime reduction, ≈25–40% maintenance-cost reduction, 6–12 month payback). Swap in your CMMS/EAM asset register (Maximo, Tango, etc.), real MTBF history, BMS/sensor feeds and your own outage cost-per-hour to make the numbers yours.
Augur · a working prototype · illustrative reliability model