Unexpected CAPEX requirements ahead of time erode profits from Renewable Investments. Tracking assets with predictive models avoids catastrophic failures that result in +500K€ asset replacement costs.

Predicting failures is possible with AI. Zero catastrophic failures should be the standard.

At BlackAxiom, we focus on bringing asset reliability to all asset managers. Our focus is on leveraging existing SCADA data to provide new levels of intelligence that provide actionable insights to asset managers to avoid major failures that erode margins and represent not only OPEX, but also new CAPEX requirements.

Predictive maintenance delivers -30% OPEX.

“Gearbox or Generator failures in Wind Turbines represent a substantial operational risk. Especially in off-shore wind, where costs of replacements are extremely high.”

The key to deploying such intelligence is in processing data in smart ways, labeling it, and making high-precision data engineering work that allows AI models to perform.

• Use clean, bias-free and high quality SCADA data to train AI models.

• Ensure a good and diverse sample dataset.

• Combine different AI model typology to hedge risks and increase reliability.

• Continuous training and refining as the quality standard.

Reducing OPEX, increasing realiability.

The major benefits from this type of predictive model are not only being able to reduce OPEX, which is a significant part. It is about the certainty and the level of reliability that the asset managers and the independent power producers obtain. Allowing them to increase capacity and rule portfolios without increasing exponential operational risk.