Grid StrAIn Part 4 — Utilities Are Betting on AI to Spot Failures Before They Happen
How condition monitoring is going from reactive to predictive—and why that matters more than ever.
Artificial intelligence is driving exponential energy demand and revealing just how fragile many grid systems really are. The result? A surge of utilities rethinking not just how they plan infrastructure, but how they maintain it.
As utilities navigate transformer shortages, aging infrastructure, and unpredictable, always-on energy use, many are investing in AI-powered condition monitoring and predictive diagnostics to stay ahead of potential failures. Across North America, operators are adopting AI-enhanced condition monitoring and predictive maintenance to detect risks earlier, act faster, and protect more with fewer resources.
It’s a shift from reactive response to intelligent prevention. And for many utilities, it’s already proving essential.
From Time-Based Maintenance to Predictive Action
Historically, most maintenance followed a time-based cycle: test equipment every X years, replace assets after Y hours. But as demand outpaces capacity and infrastructure ages in place, utilities can no longer afford to operate on fixed timelines.
Predictive diagnostics powered by AI are changing that. By ingesting long-term asset behavior data and real-time monitoring inputs, utilities can use AI to detect early warning signs and forecast failure risk with far greater accuracy.
This shift isn’t hypothetical. Utilities are already seeing results. Some are avoiding unnecessary replacements, others are preventing outages entirely. And almost all are improving their ability to prioritize where and when to act.
Smarter Tools, Smarter Decisions
For predictive AI to work, it needs a foundation of high-quality diagnostic data. That’s where condition monitoring comes in.
At Doble, solutions like Calisto™ DGA monitors, doblePRIME™ bushing and partial discharge monitors, provide the real-time insight AI needs to work. Paired with asset analytics platforms utilities can automate alerts, track fleet-level trends, and build machine learning models that guide smarter decisions.
This kind of visibility makes a measurable impact. By shifting from static testing to continuous asset health monitoring, utilities gain:
- Earlier detection of emerging risks
- More targeted maintenance and inspection schedules
- Reduced downtime and maintenance costs
- Extended asset life, especially for transformer fleets under stress
Preparing for What’s Next
AI-based condition monitoring is no longer a future vision. It’s a present-day operational strategy that’s helping utilities stretch resources, reduce downtime, and prevent grid instability before it starts.
But digital tools alone can’t overcome aging transformers, overloaded substations, and decades-old infrastructure built for a different era. As grid strain intensifies, utilities must pair smarter diagnostics with long-overdue modernization. In our next post, we’ll explore how transformer shortages, transmission bottlenecks, and permitting delays are becoming the biggest roadblocks to AI-era reliability—and what utilities can do to prepare.
At Doble, we help utilities harness the power of AI while grounding it in expert diagnostics, decades of fleet insight, and proven engineering support needed to manage today’s complexity and plan for what’s next.
Additional Information:
- Grid StrAIn: AI & Grid Reliability Part 1—The AI Energy Crunch: How Data Centers Are Reshaping Grid Reliability
- Grid StrAIn Part 2 — How Utilities Can Get Ahead of AI’s Energy Strain — Before It’s Too Late
- Grid StrAIn Part 3 — AI vs. AI: Can Artificial Intelligence Solve the Grid Strain It’s Creating?
