Challenges of Virtual Testing in Statistical Quality Control of Railway Ballast

Railway ballast is a natural product and as such it is subject to variation in petrographic
composition. This results in variations in particle shape and resistance to attrition. As existing test
methods are arduous and expensive, test intervals are typically fairly wide. As a consequence, short-term
and mid-term fluctuations of ballast quality may not be detected. This results in increased costs
for maintenance work such as tamping. Shorter test intervals would yield a better ballast quality and
reduce ballast life-cycle costs. This can be achieved with acceptable effort only if new methods are
taken into consideration. Against this background, a new statistical monitoring process for railway
ballast is proposed which combines traditional test methods with an innovative measurement
device, the Petroscope 4D®. Here, the geometrical parameters can be measured directly in a manner
superior to that of traditional tests, and the mechanical properties are statistically estimated based on
geometric and spectrographic features. This procedure is referred to as virtual testing. However,
replacing manual testing by virtual testing requires that distributions on which the prediction model
is based remain unchanged. However, there is no guarantee that, for example, new rock types that
were not included in the training phase emerge. Thus, statistical monitoring of samples from daily
production requires a novelty detection step to guarantee a high prediction performance quality.