Precision time solutions for the transport industry

Background

Qulsar’s customer was a leading organization providing services for rail infrastructure, ensuring maintenance and safety through regular inspections of a rail network in the UK. To improve their ability to complete proactive preemptive maintenance, the customer outfits special trains with high definition cameras, ultrasonic equipment and vibration sensors, and these trains travel across the UK’s rail network collecting detailed data on the track and the underlying ground. The large volume of data is stored and then offloaded at specific stations to be processed offline.

The Challenge

The customer had a challenge with organizing this growing volume of data from its sophisticated sensitive sensors in a meaningful and useful manner. The problems encountered were:

Correlation between the readings from the array of sensors.
Rich context and interrelationships between sensor data was amiss.
Some key elements were being lost by the AI/ML learning system.
Crucially, it was as if there was no effective sensor fusion - essential for predictive analytics and pinpointing the location of anomalies or faults.

Solution

Qulsar’s engineers planned to deploy a precision timing platform for its on-train sensor fusion platform. This seamless solution helped to give them real time data with precise and high quality time-stamps. These time stamps gave the data rich context and enabled the expert system to inter-relate data from different sensors. System wide actionable deductions about track health could be made and detecting the anomalies and predictive track maintenance became much simpler. There was also reduced dependence on off-line cloud processing.

The Result

In this case, Qulsar’s robust solution not only helped with a reduced deployment cost, but also helped with:

Significant increase in safety with predictive maintenance
Significant increase in track anomaly detection
Easier location of faults
Enablement of predictive maintenance reduced downtime