Utilities are under pressure to extend the life of aging infrastructure while maintaining reliability standards that leave little room for error. You’re managing transformers, breakers, poles, and underground assets that operate under increasing load and environmental stress. Traditional maintenance cycles don’t reflect real‑time conditions, which means you either overspend on unnecessary inspections or miss early signs of failure. Asset health monitoring gives you a continuous, data‑driven view of equipment condition so you can intervene before small issues turn into outages.
What the Use Case Is
Asset health monitoring uses machine learning models to assess the condition of grid assets and predict when they’re likely to degrade or fail. It sits between maintenance planning, outage management, and capital investment decisions. You’re giving your teams a live health score for each asset, updated as new telemetry, inspection data, or environmental conditions come in.
This capability fits naturally into daily operations. Maintenance planners use it to prioritize work orders. Field crews rely on it to understand what they’re walking into before they arrive on site. Capital planning teams use long‑term health trends to guide replacement schedules. The result is a more proactive, coordinated approach to asset management.
Why It Works
The model works because it captures patterns that traditional inspections miss. Many assets show subtle signs of degradation long before they fail. Temperature anomalies, vibration patterns, oil chemistry changes, partial discharge events, and load stress all contribute to asset health. AI models can process these signals continuously and surface early warnings.
This reduces friction across teams. Instead of debating which assets need attention, everyone works from the same health‑based prioritization. It also improves throughput in maintenance workflows. Crews spend less time on routine checks and more time addressing assets that are statistically at risk. Over time, this reduces unplanned outages and extends asset life.
What Data Is Required
You need a mix of structured and unstructured data. SCADA telemetry provides real‑time operational signals such as temperature, voltage, and load. Sensor data from transformers, breakers, and substations adds deeper insight into equipment behavior. Historical maintenance logs, inspection notes, and failure records help the model learn long‑term degradation patterns.
Environmental data strengthens the model. Weather conditions, vegetation encroachment, and corrosion risk factors all influence asset health. You also need accurate asset inventories with details such as age, material type, manufacturer, and installation history. Data freshness matters because asset conditions can shift quickly under heavy load or extreme weather.
First 30 Days
The first month focuses on validating whether your data is complete enough to support a reliable model. You start by selecting a small set of assets with strong historical records and consistent telemetry. Data engineers clean maintenance logs, reconcile sensor data, and align inspection histories with known failure events. You also define the health scoring framework that will guide early pilots.
A pilot model is trained and benchmarked against historical degradation patterns. Maintenance teams review the health scores during weekly planning sessions to compare them with their own assessments. Early wins often come from identifying assets that show early signs of stress but haven’t yet triggered alarms. This builds confidence without requiring immediate operational changes.
First 90 Days
By the three‑month mark, you’re ready to integrate the model into maintenance planning and field operations. This includes automating data ingestion, setting up dashboards, and creating alert thresholds for high‑risk assets. You expand the pilot to more asset classes and incorporate additional data sources such as oil analysis or partial discharge sensors.
Governance becomes important as the system scales. You define who owns model performance, who reviews health scores, and how decisions are made when the model flags a risk. Cross‑functional teams meet weekly to review accuracy, adjust thresholds, and align on upcoming maintenance windows. This rhythm ensures the model becomes part of the operational fabric rather than a standalone tool.
Common Pitfalls
Many utilities underestimate the importance of accurate asset inventories. If age, material type, or maintenance history is missing, the model learns incomplete patterns. Another common mistake is relying solely on SCADA data. While useful, SCADA alone doesn’t capture deeper degradation signals that sensors or inspections reveal.
Some teams also deploy the model without clear workflows for acting on health scores. If planners don’t know how to prioritize based on the model’s output, adoption slows. Finally, utilities sometimes overlook environmental factors such as corrosion or vegetation, which leads to health scores that look accurate on paper but miss real‑world risks.
Success Patterns
The utilities that succeed treat asset health monitoring as a continuous operational capability. They involve maintenance teams early so the model reflects real‑world conditions. They maintain strong data hygiene, especially around asset inventories and inspection histories. They also build simple workflows for acting on health scores, such as targeted inspections or accelerated maintenance.
Successful teams review performance metrics regularly and refine the model as new data becomes available. Over time, the capability becomes a trusted part of daily operations, improving reliability, reducing emergency repairs, and extending asset life.
A strong asset health monitoring capability helps you shift from reactive maintenance to proactive stewardship, delivering reliability gains and cost savings that compound year after year.