At the UK Tram Summit, ZF provided attendees with crucial insights into how ZF is utilising the power of AI and acceleration vibrations to monitor track conditions, and wheel fatigue and damage.
The AI system is underpinned by ZF’s Heavy Duty TAG sensors, that not only detect rail track surface defects and train wheel faults using inertia sensors that report irregularities, but extracts patterns from the data generated, helping customers predict the rail defect class with improved accuracy, therefore reducing maintenance downtime.
Simon Hargreaves, business manager at ZF Services UK, said: “Presenting the connect@rail system at the UK Tram Summit is a great demonstration of our technical expertise in rail, and further underlines our commitment to delivering high-end rail maintenance services. The AI-backed platform means we can offer bespoke products that meet individual customer requirements.”
The use of artificial intelligence in seeking out track and wheel faults means that common, anomalous events such as wheel-rail noise, rail corrugation or curve squeak are discounted, helping accurately identify different types of track damage.
ZF’s UK Tram Summit presentation detailed the company’s plans for further innovation to its AI platform, with further validation tests set to be completed on different rail classes and train types, with a heatmap visualisation tool one of the enhancements being developed for rail customers.
The connect@rail system determines damage to wheels using acceleration data, delivering an automated message in case of damage detection. With ZF’s Heavy Duty TAG sensors, the system is quickly implemented, with Bluetooth wireless sensors installed within the train’s axle box.
