Rolling stock - additional features
WDP - Wheel Damage Prediction
The Wheel Damage Prediction (WDP) feature is based on AI and Machine learning principles with hybrid models. WDP utilizes information from wayside detectors measuring wheel impact forces. By synchronizing it with estimated and measured distance, weather data, and tuning the performance against the maintenance records, a full-scale picture can be presented to the users, such as operation centers and maintenance planners.
WPP - Wheel Profile Prediction
The Wheel Profile Prediction (WPP) feature replicates every wheel as a digital twin tracking the condition of the individual wheel, self-correcting itself and performing analytics as new data comes in but also predicts what next measurement should be expected from the field. WPP manages measurements provided by handheld devices, one or many wayside devices or a combination of both. It can synchronize the data with distance information to gain results in distance rather than time when needed. Since it treats every wheelset as an individual it will account for different vehicle dynamics and contextual differences.
ORLOS - On-Route Load Shifting
Load distribution on wagons might shift due to a combination of vibrations, lateral and longitudinal forces during operation. This phenomena can lead to negative consequences and in worst case cause derailments. By combining data from multiple detectors measuring axle load, changes in weight distribution in both directions can be identified. The ORLOS feature can provide an early indication on occuring load shifting but also introduce further information around how the load shifts on specific routes for individual wagons and complete train sets.
BFP - Bearing Failure Prediction
DPI - Detector Performance Indication
BP - Bogie Performance
The Bogie Performance (BP) feature identifies bogies with deviating performance in terms of poor steering and asymmetric loads. The analytics processes data from multiple WILD detectors and takes multiple operational behaviors into account. The results are more robust indications on load ratios and highlighting of the worst performing bogies.