Case study card:
Using sensor data for status and maintenance prediction.
Primary Industries and Infrastructure
This project was to build the brains that connected to acoustic sensors laid at certain points along the track.
This distributed acoustic sensing creates an overwhelming amount of data, but if understood can be used for real-time observation and prediction of a) the state of a specific stretch of track, b) the location of rolling stock and other assets, and c) general event observation.
The interpretation of these massive and incredible noisy datasets presents a major challenge, but using convolutional neural networks we were able to cut through the noise and provide meaningful information for decision makers.
The client was able to use this information to understand both the status of the track and goods, as well as make predictive assessments about future maintenance requirements.