5 Ways Machine Learning Improves Asset Performance in Power Plants

Mohan Amasa | Executive, Customer Success, AssetSense

A 500 MW power plant generates about 10,000 data points every second.

It’s impossible for humans to review all this data and use it to optimize plant performance. According to a survey by the Utility Analytics Institute, respondents stated that they have a “poor” or “limited” ability to process big data with the tools and technologies available to them.[1]

However, forward-thinking power generation leaders are adopting new tools that give them a complete view of their assets’ health. Digital technologies, such as machine learning and artificial intelligence (AI), provide timely insights that help you optimize both asset and plant performance.

AI can process massive amounts of data quickly, allowing you to analyze meaningful information about your fleet in near real-time. Meanwhile, machine learning continually learns from this data and will alert you when equipment isn’t behaving normally. Armed with these insights, you can improve asset reliability, avoid costly failures, and maximize revenue.

Here are five ways that adding machine learning to your maintenance strategy can help you avoid downtime and elevate plant performance:


  • Reduce your operations and maintenance costs by moving from reactive to predictive maintenance. The insights you gain from AI and predictive analytics can help you spot problems early. With these insights, you can be proactive about asset maintenance and fix issues before they disrupt your production.
  • One nuclear plant reduced its hours spent on preventative and corrective maintenance by 37% through predictive analytics. The plant also is saving $10 million annually while improving its capacity factor and setting a record for power production.[2]


  • Prepare for the unexpected. You never know when a storm or other significant event will impact your operations. You can use the information you gain from AI and machine learning to see when assets have failed in the past and under what conditions. Then, you can optimize your disaster preparedness plans to prepare for outages when the unexpected happens.


  • Gain a holistic, real-time view of your fleet. An AI-powered asset performance monitoring platform collects and processes data in near real-time. It also supports massive volumes of data so you can analyze historical trends.
  • Overcome the operator skills shortage. According to a recent report, 48% of power professionals are concerned about an impending talent emergency, while 28% said that their company had been affected by a skills shortage. When your skilled operators retire, you can use AI to reduce human involvement or make it easier for new employees to monitor your assets.


  • Make operators’ jobs easier. User-friendly AI technology gives operators real-time insights into asset health. They can use this information to improve equipment and plant performance—while eliminating the headaches of manual processes and disparate technologies.


Want to learn more about how machine learning elevates asset and plant performance in a competitive marketplace? Get the white paper here.

[1]  T&D World: The Potential of AI for Utilities, 2019


[2] Deloitte: Digital utility asset management, building the backbone of the energy transition, 2021


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About the Author

Mohan Amasa Executive, Customer Success, AssetSense

Mohan Amasa is the Founder and CEO of AssetSense. For the past decade, Mohan’s sole mission has been modernizing asset monitoring in the industrial sector and elevating the customer experience using enterprise software. The AssetSense platform elevates asset performance and health with streamlined operations and predictive analytics. To learn more, visit