Energy

Condition monitoring tells energy operators that something changed. Causal AI tells them why, what to do about it and what each option is worth, turning alerts into prescriptive interventions.

Gas turbine prescriptive maintenance: from alerts to actionable interventions

Global integrated energy operator

$650K to $1.9M estimated annual value per high-criticality turbine, payback typically within 6 to 12 months

Challenge

A fleet of high-value industrial gas turbines had condition monitoring capable of detecting abnormal sensor behavior but unable to distinguish causes from symptoms or guide intervention decisions. Teams received alerts without actionable guidance, resulting in unnecessary outages and degradation that progressed further than necessary.

Approach

A causal turbine degradation model was built from vibration, acoustic, thermal, pressure, flow and operational data combined with maintenance and outage history. Rather than generating a risk score, the system compared specific intervention options and estimated the financial impact of each course of action.

Outcome

The operator shifted from reactive alert management to prescriptive intervention planning. More maintenance aligned to planned windows, unnecessary outages were avoided, and every maintenance decision was supported by a quantified financial justification and a clear causal rationale.

Causal AIML/DLKnowledge GraphDeterministic Code

Renewable portfolio performance: identifying the true drivers of generation loss

Global renewable energy portfolio operator

Challenge

Generation across a solar portfolio was consistently below modeled expectations, with unexplained variance between apparently similar sites. Maintenance decisions were made without knowing whether underperformance was caused by equipment condition, operating settings, local weather effects or some combination.

Approach

A causal model connected generation telemetry, equipment condition, weather observations, maintenance records and curtailment events. It separated causal drivers of generation loss from correlated noise, so interventions could be evaluated against expected yield impact before committing resources.

Outcome

Root causes of underperformance were identified and quantified for each site. Maintenance became better targeted, overall portfolio yield improved against modeled expectations, and the operator established a like-for-like performance comparison framework across the asset base.

Causal AIML/DLDeterministic Code

The right problem. The right AI. Working in weeks.

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