DTECH 2024: Enterprise Asset Management in Utilities Get a Powerful Lift From AI and IoT
Connected intelligence is transforming how utilities predict equipment failures and optimize asset performance across their operations
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Key takeaways
Connected intelligence is transforming how utilities predict equipment failures and optimize asset performance across their operations
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When AI and IoT work together, good things happen in Enterprise Asset Management (EAM). When evolved EAM is applied to utilities; good things become great solutions.
Artificial Intelligence (AI) is revolutionizing asset management in the utility sector, enhancing operational efficiency, reliability, and cost savings. AI enables predictive maintenance, allowing utilities to anticipate equipment failures before they occur, minimizing downtime and maintenance costs. Through intelligent asset monitoring, AI systems utilize sensors and computer vision to detect anomalies and assess asset health in real time, reducing the reliance on manual inspections. Additionally, AI optimizes energy distribution and grid management by analyzing consumption patterns and making real-time adjustments, ensuring a stable and efficient power supply. Integrating AI in utility asset management streamlines workforce management and resource allocation and paves the way for a more resilient and sustainable energy infrastructure.
When you combine EAM with APM, IoT, and AI, all the acronyms merge together, and now you really have something.
— Carol Johnson, VP of Energy, Utilities, and Resources at IFS
At DISTRIBUTECH 2024, MarketScale spoke with Carol Johnson, the VP of Energy, Utilities, and Resources at IFS, who shared her insights on the transformative power of combining Enterprise Asset Management with Advanced Predictive Maintenance (APM), IoT, and AI.
Carol's Thoughts
"EAMs have been around for a long time, which is Enterprise Asset Management, but when you combine EAM with APM, IoT, and AI, all the acronyms merge together, and now you really have something.
When you bring in Internet of Things data, you get real-time performance monitoring, so you now know the health of that asset in real time. But when you bring AI to it, it can actually analyze and predict the likelihood of that asset to fail and when—so catching, you know, those blips of those anomalies, when they occur, and using that big-picture data to project what's going to happen with that asset over a period of time. And then being able to put that into a scheduling engine and a field solution to be able to go out and react and do something about that.
From an asset management perspective, they're dealing with a lot of critical infrastructure out there. They need things that will not only be regulatory compliant, especially if you're a nuclear power plant; for example, there are a lot of regulations around how you store and maintain and who gets access to that data.
— Carol Johnson, VP of Energy, Utilities, and Resources at IFS
So, security is a big, huge problem. And then certainly having, you know, historical data coming in from drones or from smart devices on the grid, you now have, you know, real-time asset information coming in that you need to store and maintain."
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