Detection of wheel faults with smart algorithms
Wheel faults lead to high stress on the railway infrastructure and generate noise. SCS has developed algorithms to detect wheel faults. Thus, affected vehicles can be rehabilitated accordingly.
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Initial situation: Wheel fault
Wheel defects (e.g. flat spots, break-outs) lead to high stress on the infrastructure and generate noise. For the detection of different wheel defects, different data must be linked and evaluated.
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Solution SCS
SCS has tested and implemented various algorithms for detecting wheel faults. The methods are based on the signals provided by the RLC.
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Benefit SBB
The presence of wheel faults is detected. For certain wheel faults, an estimate of the severity of the wheel fault is made. Affected vehicles can be reported to the RU so that the RU can rehabilitate the vehicles.
As part of the CCUs (train control equipment), RLC systems (wheel load checkpoints) measure the weight of passing vehicles. The signal curves are influenced by wheel faults. Thus, the signal curves can not only be used to determine the weight, but also show whether wheel faults are present.
In the first approach for determining a wheel error, an easily determined parameter (dynamic coefficient) is calculated from the signal curve of each wheel. If the dynamic coefficient is small, there is no wheel error and if the dynamic coefficient is large, there is a pronounced wheel error.
In the second approach, an algorithm developed by SCS detects flat spots and estimates their length. This takes advantage of the fact that a flat spot that occurs exactly above a sensor results in a typical signal curve. The algorithm searches for this typical signal course on the measurements. If one is found, it is analysed in more detail by the algorithm in order to estimate the length of the flat spot. To estimate the length, characteristic points must be determined on the signal course.
In the third approach, the "unwinding" of the wheel is determined, i.e. the outside of the wheel and its condition is shown on a graph. This gives a condition of the wheel over the entire circumference and the individual defects on the tread are made visible. Since the sensors available on the RLC system do not record the entire circumference of the wheel, additional sensors are used for processing. The algorithms developed by SCS process the measured data from the individual sensors and then combine them for unwinding.
Through the three approaches, existing wheel faults can be detected and analysed in more detail. Based on the analysis, the recommendation for the rehabilitation of vehicles with wheel faults can then be issued.
Cover photo: Heitersberg [CC-BY-SA-4.0] via Wikimedia Commons (cropped)
