Leveraging digital twin predictive modeling with integrated artificial intelligence, machine learning and software analytics, the Tube Watch system represents—in real time—the working condition of a system’s components. This means, it remotely monitors and analyzes tube health trends over time, and any indications of change in performance.

Predictive analytics consolidates the data gathered from the system and its components to simulate real-world situations, through machine learning, to obtain insights into what might happen in the future. This allows it to predict the estimated failure date with high accuracy and help you make a determination whether or not to proactively replace the X-ray tube.

A GE engineer connects remotely into the system to run an in-depth assessment and, if needed, will schedule on-site maintenance at a time that is most convenient for the facility.

Once the tube is replaced, the system is back up and running with minimum disruption and workflow impact.