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Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype

Kauschke, Sebastian and Fürnkranz, Johannes and Janssen, Frederik (2016):
Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype.
In: Discovery Science - 19th International Conference, DS 2016, Bari, Italy, October 19-21, 2016, Proceedings, Cham, Springer, Bari, Italy, October 19–21, 2016, ISBN 978-3-319-46306-3,
DOI: 10.1007/978-3-319-46307-0_10, [Conference or Workshop Item]

Abstract

In cargo transportation, reliability is a crucial issue. In the case of railway traffic, the consequences of locomotive failure are not limited to the affected machine, but are propagated through the railway network and may affect public transport as well. Therefore it is desirable to predict and avoid failures. In order to do this, constant monitoring of the trains’ systems and measurement of the relevant variables is required, but often not implemented. In this paper we leverage the existing technology of the 185 locomotive series and build a layered model for power converter failure prediction that can be applied without additional technology. We train instance anomaly detectors based on the pattern structure of the locomotives’ diagnostic messages from historical data records. For this purpose we selected rule and decision tree learning because they can be easily implemented in the existing software, whereas more complex classifiers would require costly software adaptations. In order to predict a time series of instances, we construct a meta classification layer. We then evaluate our model on the data of 180 locomotive tours by leave one out classification. The results show that the meta classifier improves classification accuracy, which will allow us to use this technology in a fielded prototype installation without disturbing daily operations.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Kauschke, Sebastian and Fürnkranz, Johannes and Janssen, Frederik
Title: Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype
Language: English
Abstract:

In cargo transportation, reliability is a crucial issue. In the case of railway traffic, the consequences of locomotive failure are not limited to the affected machine, but are propagated through the railway network and may affect public transport as well. Therefore it is desirable to predict and avoid failures. In order to do this, constant monitoring of the trains’ systems and measurement of the relevant variables is required, but often not implemented. In this paper we leverage the existing technology of the 185 locomotive series and build a layered model for power converter failure prediction that can be applied without additional technology. We train instance anomaly detectors based on the pattern structure of the locomotives’ diagnostic messages from historical data records. For this purpose we selected rule and decision tree learning because they can be easily implemented in the existing software, whereas more complex classifiers would require costly software adaptations. In order to predict a time series of instances, we construct a meta classification layer. We then evaluate our model on the data of 180 locomotive tours by leave one out classification. The results show that the meta classifier improves classification accuracy, which will allow us to use this technology in a fielded prototype installation without disturbing daily operations.

Title of Book: Discovery Science - 19th International Conference, DS 2016, Bari, Italy, October 19-21, 2016, Proceedings
Place of Publication: Cham
Publisher: Springer
ISBN: 978-3-319-46306-3
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Knowl­edge En­gi­neer­ing
20 Department of Computer Science > Telecooperation
LOEWE
LOEWE > LOEWE-Schwerpunkte
LOEWE > LOEWE-Schwerpunkte > NiCER – Networked infrastructureless Cooperation for Emergency Response
Event Location: Bari, Italy
Event Dates: October 19–21, 2016
Date Deposited: 15 Mar 2017 21:16
DOI: 10.1007/978-3-319-46307-0_10
Additional Information:

ISBN 978-3-319-46307-0

Identification Number: TUD-CS-2016-14710
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