Library — Energy and infrastructure

Ageing infrastructure, read in real time

Grids, pipelines and plants were built to last decades, and many have. Keeping them running now depends on knowing which assets are closest to their limits.

01The problem

Critical infrastructure is rarely short of data. It is short of a way to read it together.

SCADA readings, inspection records, maintenance histories and outage logs accumulate in separate systems. What is missing is the reading that sees a transformer running warm, a deferred inspection and a forecast spell of extreme heat as one risk rather than three unrelated entries.

Maintenance budgets are finite and the asset base keeps ageing. The operators who spend them best are the ones who can show, with evidence, which assets matter most, which are declining fastest and what a failure would reach.

Infrastructure rarely fails without warning. The warning is just spread across too many systems to hear.

02Where Theralon applies

  1. 01

    Condition across systems

    SCADA telemetry, inspection results and maintenance history combined into one reading of each asset’s state.

  2. 02

    Criticality, not only condition

    A declining asset weighed by what depends on it — sites, customers, other assets — rather than by its condition alone.

  3. 03

    Load and weather

    Asset condition read against forecast load and conditions, so stress is anticipated rather than recorded afterwards.

  4. 04

    Maintenance where it counts

    Finite maintenance effort directed to the assets where it removes the most exposure.

  5. 05

    Data worth trusting

    Sensor gaps, stuck readings and inconsistent records flagged, so a decision is never built on a faulty instrument.

03What changes

Condition in one system, criticality in another

Both in a single reading

Maintenance by calendar

Maintenance by exposure

Every sensor trusted by default

Data validated before it is used

04Under the hood

Integrity Intelligence validates operational data itself — anomalies, missing events and unexpected behaviour — so the forecasts built on top of it rest on inputs that can be trusted. Deployment region is an explicit, disclosed setting, and every change to the model is recorded in a single tamper-evident history.

If this is your operation, we would like to hear about it.

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