In Stage 4, we take everything that we’ve learned to help your business build connected asset models at scale, on the edge to predict the remaining useful life of a part, and compare it to the actual useful life. This allows you to identify issues across large, dispersed asset populations and adjust accordingly. When connectivity is an issue, self-sufficient edge devices can act quickly.
Our demo shows an edge device on trucks with intermittent cloud connectivity. It can rely on its own computing power when not connected and is running a machine learning algorithm to predict the engine’s remaining useful life. It can also detect anomalies, out of range sensor values, predict failure, and in many cases trigger actions on-board to address potential problems before they escalate to avoid costly emergency repair scenarios.
Return to the Intelligent Device Journey Overview.