Gridnostic and the Science of Severity: A New Way to Assess Asset Risk
< Back to Articles & Case Studies
At the heart of the Gridnostic platform lies the Severity Diagnostic Methodology – a structured way to transform inspection data into objective, prioritized insights that utilities can rely on.
Utilities are under constant pressure. On one side, aging assets and rising demand strain reliability. On the other, regulatory requirements and limited budgets demand smarter, more strategic decisions. Asset managers must decide: which components need urgent attention, which can be scheduled for later, and which are safe to monitor?
The challenge isn’t the lack of data – modern inspections capture huge amounts of imagery from UV, thermal, and visual sensors. The challenge is transforming that raw data into consistent, objective, and actionable insights. That’s exactly what Gridnostic’s methodology is designed to do.
From Subjective Impressions to Objective Scores
Traditionally, interpreting inspection imagery has been highly subjective. Two inspectors looking at the same image could reach very different conclusions, influenced by their experience, expertise, or even the conditions of the day. Studies show that even the same inspector might score the same anomaly differently at different times.
Gridnostic changes this. Its Severity Diagnostic Tool provides a step-by-step interface that guides inspectors through structured evaluations, minimizing room for personal bias. AI co-pilot tools further support this process by assisting with image analysis, detecting relevant features, and suggesting classifications.
Most importantly, the tool translates qualitative observations into quantitative severity scores. These scores are developed based on research and guidelines from the Electric Power Research Institute (EPRI). The result is a standardized, repeatable process: imagery no longer produces scattered opinions, but reliable diagnostic data.
Beyond the Component: The Importance of Context
A defect on its own tells only part of the story. For example, a small crack on an insulator in a lightly loaded rural line may pose minimal risk. The exact same defect on a heavily loaded transmission line serving thousands of customers, however, could have serious consequences.
This is why Gridnostic’s methodology integrates Asset Impact Parameters. These encompass tens of contextual factors that influence risk and prioritization — and the evaluation is performed entirely with AI.
Examples of such parameters include:
- Number of customers connected to the asset.
- Line redundancy and backup options.
- Regulatory and compliance risks.
- Environmental stressors such as pollution, fire risks, or salt exposure.
By automatically bringing this context into the analysis, Gridnostic ensures that condition scores are weighted against system-level consequences, giving utilities a reliable foundation for prioritization.
How It Works — Gridnostic Severity Scoring at a Glance
Bringing Condition and Context Together
What makes Gridnostic’s Severity Diagnostic Methodology powerful is the way it combines two perspectives into one outcome:
- The condition of the component, determined from inspection imagery and guided analysis.
- The impact of the asset, based on dozens of contextual parameters automatically evaluated by AI.
By merging these two dimensions, the methodology produces a severity score that reflects not only how bad the defect is, but also how much it matters to the grid.
This approach ensures that a minor anomaly on a critical asset is not overlooked, while less consequential issues are managed appropriately. The result is a clear, prioritized view of where maintenance efforts should go first.
Contact
Schedule a meeting
