Predictive maintenance for fossil power producers

Unexpected downtime leads to painful production losses and unplanned spending for fossil fuel power producers. To remain competitive in the growing energy landscape, allowing critical assets run to failure is not an option.

AI-powered predictive maintenance offers an advanced data-driven solution to reduce unexpected downtime, drive efficiency, and improve profitability for fossil fuel power plants.

SparkCognition’s AI-powered solution, based on the SparkPredict® predictive analytics product, uses machine learning to return actionable insights, so you can:

  • Minimize unexpected equipment failures
  • Reduce maintenance costs and extend equipment life
  • Safeguard employees and company assets
  • Prevent catastrophic events that threaten entire operations, worker safety, and the bottom line

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

Identify unexpected system failures

Machine learning unlocks insights from your data with speed, efficiency, and accuracy. SparkCognition’s solution analyzes large volumes of asset data and uncovers causal relationships using advanced unsupervised learning techniques—identifying patterns that would indicate problems or impending asset failures.

Reduce unscheduled downtime

Fossil power producers can make the most of the data they already have, using predictive maintenance models to head off unplanned downtime before it happens. Flag any values that deviate from the norm, and pinpoint exactly when and how an asset failure will take place.

Increase failure identification lead time

Fossil fuel power production plants and assets inevitably deteriorate over time. In past deployments, SparkCognition has increased the ability to identify unexpected subsystem failures on the order of days or weeks in advance.

Rapidly scale predictive models across your organization

Machine learning models adapt over time, eliminating the labor and costs needed for manual updates and model maintenance. Using our solution, custom alerts tuned to fossil power producers’ key operational metrics work together to preserve uptime and safety.

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

While today’s power-generation sector faces disruption from the rapid growth of renewable energy sources and fluctuating energy demand, McKinsey & Company reports that “gas and coal-powered plants are likely to remain central to the global energy supply for at least the next decade.”

Even so, fossil fuel power producers can’t accept unexpected asset failures that grind operations to a halt. Each event comes with a high price tag in terms of lost production and repairs—not to mention safety concerns.

To remain successful, fossil fuel power plants need resilient, streamlined operations at scale. The need for a comprehensive AI strategy employing predictive maintenance and machine learning has never been clearer.

Our solution uses advanced unsupervised learning techniques to help fossil power producers:

  • Analyze large volumes of data
  • Identify anomalous behavior
  • Understand causal relationships


In an industry full of uncertainty, machine learning technologies are already allowing fossil power power producers to fully leverage the potential of their data—enabling safer and more predictable operations.

Learn more about how we helped a major power producer prevent critical asset failure by reading the full case study.

SparkCognition is committed to compliance with applicable privacy laws, including GDPR, and we provide related assurances in our contractual commitments. Click here to review our Cookie & Privacy Policy.