Smart Train Crossing

Background

Detecting damaged wheels and other issues that may occur during heavy train transports. Need for advanced edge analytics and sending data to the cloud for historical usage and AI-training. Global control and management of deployed edge software.

Prerequisites

Remote locations and bandwidth constraints require local buffering (store data if/when the devices lose connectivity). Streaming sound frequency analysis with large volumes of data to 3rd party service systems and maintenance programs.

Solution

Crosser Node deployed in a digital box connected to the crossing:

• Streaming sound frequency analysis algorithm deployed in a regular data flow
• Integration to Crossing execution system and notification system
• Sending data to cloud service for AI training and storage

Crosser Cloud:

• Customer organization set up with roles
• Resources library setup