ABSTRAK Prasidya Wikaranadhi
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PUBLIC Irwan Sofiyan PUSTAKA Prasidya Wikaranadhi
PUBLIC Irwan Sofiyan
Track irregularities are the main disturbance to rail vehicle dynamics systems.
Severe track irregularities will subject the passing vehicles to safety risks and
unfavourable riding condition. Thus, track irregularities must be assessed regularly
and kept within an acceptable level.
Currently, track irregularities are commonly assessed based on measurement data
obtained from special track measurement vehicles. However, track irregularity
assessment methods based on track geometry measurement often show poor
correlation with the actual vehicle response running on the track. Alternative
methods based on vehicle response have been an emerging research focus, aiming
to overcome the shortcomings of geometry-measurement-based methods.
Track irregularity assessment based on vehicle acceleration works by establishing
the correlation between vehicle response (dynamic output) to track irregularities
(dynamic input). Studies done so far have focused on analysing the different types
of track irregularities separately. Combined track irregularities have yet to become
a focus of study, despite possible cross correlation affecting vehicle response. The
present work aims to examine the use of machine learning methods to analyse
combined track irregularities based on vehicle response.
Rail vehicle response data generated through dynamic multibody simulation is
filtered and processed into datasets of six features comprising standard deviation
and peak value of carbody lateral acceleration, vertical acceleration, and roll
acceleration per section of 100 metres. For machine learning training purpose, track
irregularities dataset containing standard deviation and peak value of longitudinal
level, alignment level, and cross level irregularities is also constructed.
Machine learning classification is done with all six features as the predictors and
both single-type irregularity class label and combined class label as the response,
separately. Five classification algorithms are used: Decision tree, linear SVM,
logistic regression, kNN, and random forest.
Machine learning classification model training result shows high validation
accuracy value across all classification cases and algorithms. Combined
classification test result also presents high accuracy value.
Further simulations are done with variance in rail vehicle speed, carbody mass, and
wheel-rail friction coefficient. Previously trained combined classification model
can predict the track irregularities condition in those condition with reasonable
accuracy. This shows that the classification model is not overly sensitive to change
in operational parameter, suggesting a high probability of success for use with ontrack
measurement data.
Track irregularities classification result is interpreted using Shapley additive
explanation (SHAP). The Shapley explanation of the analysed track section
indicates the most important predictors that explain the classification result. This
indication can be used to analyse the type of track irregularities that is in
unacceptable condition on that track section.