Smart Transformer IoT & AI monitoring
A reference architecture for adding continuous condition monitoring and service revenue to a transformer product line.
Power and distribution transformers often provide little operating data between inspections and a visible fault. That limits the OEM's ability to offer condition-based service, monitoring subscriptions, or warranty intelligence.
Design a white-label monitoring offer that covers sensing, edge communications, the asset data model, and analytics while remaining operable by the transformer OEM.
The solution, layer by layer.
Reference sensing-to-AI architecture
A four-layer blueprint covers sensing, edge communications, the data layer, and analytics as one OEM product line.
DGA & Duval fault analysis
Dissolved-gas analysis with Duval-method classification, aligned to IS, IEC and IEEE standards, flags incipient faults before they cascade.
Condition-based & predictive maintenance
Maintenance decisions use transformer condition and trend rather than relying only on a fixed interval.
Three OEM revenue models
The architecture supports premium connected hardware, monitoring subscriptions, and predictive AMC or warranty services around the existing product line.
Figures are recorded outcomes or project targets. The status label identifies reference architectures and proposals.
Have a problem shaped like this one?
Bring the asset, its current data path, and the decision that is still being made manually. We will map the first useful scope.