RAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD

Railway track irregularities are the deviation of rail from their nominal geometry. Severe track irregularities can reduce driving comfort and safety. Currently, a particular measurement vehicle must be used to measure track geometry for the railway assessment, which has substantial investment costs...

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Bibliographic Details
Main Author: Deo Alfian, Stefanus
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/69008
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:69008
spelling id-itb.:690082022-09-19T21:21:02ZRAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD Deo Alfian, Stefanus Indonesia Final Project Railway irregularity, machine learning, threshold shifting. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/69008 Railway track irregularities are the deviation of rail from their nominal geometry. Severe track irregularities can reduce driving comfort and safety. Currently, a particular measurement vehicle must be used to measure track geometry for the railway assessment, which has substantial investment costs and stops train operations. One of the sources of the vehicle dynamics response is the rail geometry, but the two have a poor correlation. Some methods were used to map the correlation between vehicle dynamics response and track geometry, one that is currently under development is machine learning. Nowadays, some machine learning research on railway irregularity is limited to classifying a track section into discrete classes. In addition to discrete classification, this study will present the probability of an irregularity occurrence. Dataset is generated through dynamics simulation with predefined irregularities. Vehicle response is represented in form of carbody vertical and roll acceleration. Input features for machine learning are velocity as well as standard deviation and peak value of carbody vertical and roll acceleration, while the output is based on the acceptance level of standard deviation and peak value of irregularity. Logistic regression and neural network algorithms are chosen for their ability to describe the classification probability of each track section as well as threshold shifting. Both models managed to provide 90% accuracy with 90% precision and 93% recall. That performance shows that both models were suitable for this case. Further study is done by varying the threshold value between 0.2 and 0.8. Threshold shifting was useful to optimize the precision and recall value according to the needs. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Railway track irregularities are the deviation of rail from their nominal geometry. Severe track irregularities can reduce driving comfort and safety. Currently, a particular measurement vehicle must be used to measure track geometry for the railway assessment, which has substantial investment costs and stops train operations. One of the sources of the vehicle dynamics response is the rail geometry, but the two have a poor correlation. Some methods were used to map the correlation between vehicle dynamics response and track geometry, one that is currently under development is machine learning. Nowadays, some machine learning research on railway irregularity is limited to classifying a track section into discrete classes. In addition to discrete classification, this study will present the probability of an irregularity occurrence. Dataset is generated through dynamics simulation with predefined irregularities. Vehicle response is represented in form of carbody vertical and roll acceleration. Input features for machine learning are velocity as well as standard deviation and peak value of carbody vertical and roll acceleration, while the output is based on the acceptance level of standard deviation and peak value of irregularity. Logistic regression and neural network algorithms are chosen for their ability to describe the classification probability of each track section as well as threshold shifting. Both models managed to provide 90% accuracy with 90% precision and 93% recall. That performance shows that both models were suitable for this case. Further study is done by varying the threshold value between 0.2 and 0.8. Threshold shifting was useful to optimize the precision and recall value according to the needs.
format Final Project
author Deo Alfian, Stefanus
spellingShingle Deo Alfian, Stefanus
RAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD
author_facet Deo Alfian, Stefanus
author_sort Deo Alfian, Stefanus
title RAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD
title_short RAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD
title_full RAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD
title_fullStr RAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD
title_full_unstemmed RAILWAY VERTICAL IRREGULARITY DETECTION BASED ON CARBODY DYNAMIC RESPONSE USING MACHINE LEARNING METHOD
title_sort railway vertical irregularity detection based on carbody dynamic response using machine learning method
url https://digilib.itb.ac.id/gdl/view/69008
_version_ 1822990780703178752