Predictive maintenance for wind power producers

Wind power producers need to be able to monitor component reliability, identify and correct actionable sources of underperformance, and optimize maintenance scheduling. Moreso, they need to achieve this in a cost-effective and scalable manner in a highly demanding market.

SparkCognition’s AI-based predictive analytics solution efficiently analyzes real-time operational data at scale, recognizes asset failures in advance, and identifies underperformance using machine learning to return actionable insights.

For example, wind power producers can use our solution to determine, with confidence, whether a turbine is in its normal state or experiencing a performance issue versus a component issue. With this information, operators can assess the next steps and perform maintenance to prevent potential subsystem asset failures.

SparkCognition’s solution enables wind power producers to:

  • Increase identification of unexpected subsystem failures
  • Increase failure identification lead time
  • Optimize maintenance scheduling
  • Reduce risk exposure for employees

It delivers the power of expecting the unexpected.

“Cutting-edge technologies, like AI-based predictive analytics, are key enablers to improving the efficiency of our operations and meeting our ambition to become a net-zero company by 2050 or sooner. Working collaboratively with SparkCognition, we have delivered this project in an agile way,”

– Fereidoun Abbassian, former VP of Transformation, Upstream Technology at bp

Increase asset availability

Continuously monitoring component reliability protects wind power producers from incurring damaging generation losses. Past deployments in other industries have increased asset availability by up to 4% across massive operations.

Prevent catastrophic asset failures

Receive notice of suboptimal operations and impending asset failure in remote locales days or weeks in advance—with minimal false positives—enabling a maintenance program tuned to emerging needs rather than preconceived timelines. Past deployments have provided asset failure predictions with an average of nine days of advance warning.

Quickly build more accurate, cost-efficient models

Automated model building enables data scientists and non-technical users to create highly accurate models using their production data. Build models to fit your assets as they are, without requiring the purchase of new parts.

Improve efficiency, safety, and profitability

Machine learning-based anomaly management and predictive analytics help wind power producers proactively identify anomalous behaviors over time—enabling better planning of maintenance and work orders, lowering costs, and reducing risk to workers onsite.

SparkCognition solutions have been used to reduce operating cost and improvement in efficiency by 2-5%

Whenever unexpected maintenance problems arise at wind farms, key workers are exposed to elevated risk and operating costs go up as a result of unplanned repairs and suboptimal performance. That’s a serious problem in a volatile and highly competitive energy market. Today’s leading wind power producers need the most resilient, streamlined operations to remain successful.

By leveraging AI, wind power producers can unlock an entirely new, data-driven approach to efficient and competitive operations.

SparkCongition’s predictive maintenance solution holds the answer to operational challenges present in today’s wind energy industry, making the future of renewable wind power more efficient and promising for the long term.

Read the full case study to learn about how our AI solutions correct suboptimal wind operations.

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