Analytics

Rolling stock - additional features

Analytics

Predge Rolling Stock Analytics features processes incoming data continuously to add further value to customers data generation. Hybrid models is the strategy for wear and fatigue driven failures with the aim of predicting events before they occur. An extensive understanding of root causes and a strong focus on validation of indications against documented events results in high hit rate with low number of false positives for our analytics.

ANalytics 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.

ANalytics features

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.

Analytics features

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.

ANalytics features

BFP - Bearing Failure Prediction

The Bearing Failure Prediction (BFP) feature uses data from multiple bearing acoustic detectors and/or hotbox detectors. It provides aggregated indications to identify noise and bearings with an increased temperature at an early stage. The indications are based on deviating behavior for each parameter, vehicle type and location and can be tailored to match the expected capacity of bearing revisions and withdrawals.

ANalytics features

DPI - Detector Performance Indication

Understanding the data quality is a necessity when utilizing information as decision support. DPI provides a quality measure for incoming data sources and performance indications to compare sources, understand seasonal variations and detect deviations.

ANalytics features

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. 

ANalytics features

FLE - Flat Length Estimation

Detect and estimate the wheel flat based on dynamic data received from wheel impact load detectors. FLE classifies wheel flats in wheel impact force data and estimates their length in millimeters.

Analytics features

VDE - Vehicles Distance Estimation

By analyzing data from detectors along the train tracks the VDE feature can calculate and keep you up to date on your wagons’ position and distance travelled.

Additional features

The additional features enhances the business value provided by the core and allows the customer to stay in forefront of technology:

Additional features

The additional features enhances the business value provided by the core and allows the customer to stay in forefront of technology: