Condition intelligence for the rotating equipment that delivers reliable power.
SibylCor helps power generation owners and operators understand developing conditions across critical rotating equipment earlier—so they can reduce unplanned outages, improve availability, and operate with greater confidence and efficiency.
The equipment behind new capacity is already constrained.
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116 GW
of gas-power equipment backlog and slot reservations at GE Vernova in Q2 2026, up from 100 GW.
Source: GE Vernova, Q2 2026 financial results (July 22, 2026) (opens in a new tab)
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30%
more gas-turbine and reciprocating-engine capacity ordered in 2025 than OEMs could deliver, on record orders of 100 GW.
Source: Rystad Energy, Gas Turbine Report H2 2026 (opens in a new tab)
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5+ years
from order to delivery for large natural-gas turbines. Smaller turbines now take 18 to 36 months.
Source: EPRI, via Utility Dive (March 23, 2026) (opens in a new tab)
This shifts the value of diagnosis earlier in the maintenance cycle. Identifying a developing bearing, blade, combustor or compressor fault with sufficient lead time allows engineers to reserve parts, align service specialists and schedule work while the plant still has operating flexibility.
The tools stop at the signal.
Anomaly detection can tell you something moved. It cannot tell you what—or how sure it is.
Anomaly detection
- Tells you something moved
- Cannot tell you what, or how sure it is
- Leaves the operator to supply the confidence through inspection and downtime
- Answers stitched together across systems that were not built to talk to each other
Diagnostic reasoning
- Names the failure mode and the component
- States the confidence and the evidence behind it
- Gives a prognosis for how degradation may progress
- Recommends the action and when to take it
Planning horizons
- 2–3 years advance outage planning recommended for 7F major upgrades, as lead times rise
Critical rotating equipment across the plant
Critical fleet availability, fuel and thermal performance, and major rotating equipment, using the sensors, historians, controls and maintenance systems already in place. No proprietary hardware.
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Turbines & Generators
Gas turbines, reciprocating engines and the generators they drive
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Pumps & Fans
Feedwater, cooling-water and fuel pumps; fans and cooling-tower drives
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Support Equipment
Fuel-gas compressors, motors and supporting rotating plant
Conventional monitoring eventually knows something is wrong, but not what.
Physics-based equipment models calculate parameters that cannot be directly measured and reconcile them with measured operating data. Machine learning and diagnostic reasoning evaluate those relationships to identify the failure mode and quantify diagnostic confidence.
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Observed
Power, efficiency or heat rate drifts in a way the model cannot explain away, before anything reaches an alarm state.
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Reconciled
Virtual sensors—calculated parameters that cannot be measured directly—are checked against the physics of the equipment.
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Ranked
Competing failure modes are tested against live evidence, each with a component and a confidence.
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Recommended
Inspection confirms a named diagnosis, and the shutdown is planned for when it is most cost effective.
What this changes
- Inspect with a diagnosisShutdown and inspection start from a named failure mode, not a search for one.
- Reserve long-lead partsHot-gas-path parts, rotor components, blades and repair slots are secured early.
- Plan the outageA costly unplanned outage becomes a scheduled one.
The earlier the developing failure is understood, the more manageable the exposure becomes.
The repair bill is only the beginning.
Protecting productive capacity is becoming a strategic reliability decision, not just an annual maintenance expense.
Critical equipment failure creates a loss stack: lost productive capacity, replacement power, repair and overhaul, reduced redundancy and constrained recovery.
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Reduce unplanned outages
Developing faults are diagnosed while the plant still has operating flexibility.
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Improve availability
Work is consolidated into planned windows instead of forced shutdowns.
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Protect capital
A gas turbine can represent tens or hundreds of millions of dollars in equipment value.
SibylCor is designed to compute the Total Cost of Risk for each monitored asset: process unavailability, degraded efficiency, emergency procurement and missed operating windows.
Equipment value: Wood Mackenzie end-2027 gas-turbine price forecast of US$600/kW (April 2026) applied to representative turbine classes.
“Four‑ to five‑year lead times are likely to continue for now, and reliability in that environment depends on early, informed decisions.”
Independent industry context. Quotation does not imply endorsement of SibylCor.
Who we’d like to talk with
Generation owners and operators
Fewer forced outages and more done in every planned window.
Plant reliability and performance engineers
A named failure mode, its evidence and its confidence behind every alert.
On-site and behind-the-meter power developers
Operator-managed turbine and engine availability, from commissioning onward.
We’d welcome a conversation about the equipment and maintenance challenges that matter most to your fleet.
Or email info@SibylCor.com
Start with one critical asset class
One critical asset class, one narrow engagement—starting where the consequence of failure is already understood, with a low-risk path to validate SibylCor.