From Data Collection to Insight
Modern substations generate vast amounts of data—temperatures, gas levels, vibrations, contact wear, and breaker operations. Historically, much of it went unused. Now, with advances in edge computing and AI, that data can be analyzed in real time to forecast failures before they happen.
A digital twin models the behavior of a physical asset, updating continuously with sensor input. When combined with machine-learning algorithms, it becomes a powerful tool for predictive maintenance.
How Predictive Maintenance Works
AI systems learn normal operating patterns from historical data and flag deviations that may signal early degradation. This approach replaces fixed maintenance intervals with condition-based action, saving time and resources.
Key applications include:
- Transformer oil-gas analysis for insulation breakdown detection.
- Circuit-breaker wear prediction using contact resistance and operation counts.
- Partial-discharge pattern recognition in cables and bushings.
- Thermal-imaging analytics for hotspot detection in switchgear.
Each insight allows maintenance crews to intervene...
