Improving Cargo Train Availability with Predictive Maintenance: An Overview and Prototype Implementation
| Type of publication: | Inproceedings |
| Citation: | kauschke2016aet |
| Booktitle: | AET Papers Repository |
| Year: | 2016 |
| Month: | October |
| Publisher: | Association for European Transport |
| ISSN: | 2313-1853 |
| URL: | http://abstracts.aetransport.org/paper/index/id/4857/confid/21 |
| 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. Beside the cost of the machine itself, delays are caused and ultimately propagated through the railway network, rendering the accumulated cost of a single incident unpredictable. In order to avoid failures, Predictive Maintenance (PM) can be used. Predictive Maintenance targets the substitution of existing maintenance processes (e.g. time-based preventive maintenance) by conveniently scheduled corrective maintenance through exploitation of the underlying deterioration processes. In an ideal PM scenario, constant monitoring of the machine is available, measuring all relevant variables, e.g., temperatures or vibrations on a regular basis. However, in the real world, this assumption is limited: The hardware often does not deliver the required amount of data in the necessary precision. Often the machines record only a log-file which provides all activities—useful or not—that the various systems in the machine keep track of. In this paper, we give a short overview on PM in general and on the various types of systems that can be considered for PM.We elaborate on the differences in data as well as the nature of the systems it is possible to predict failures upon. In a prototypical example, we make use of machine learning methods to construct a failure prediction model for cargo trains. This data-driven approach focuses on a specific failure problem which is important to improve upon and aims at an easy prototype implementation for the currently available system. We train a classification model which uses the pattern structure of the diagnosticmessages of the locomotive to recognize abnormal activities in the locomotives behaviour. A meta-classification layer on top of this anomaly detection allows us to build a predictor with little false positives.We evaluate our findings on the data of 180 locomotive tours and elaborate on possible improvements of the method. |
| Keywords: | Predictive Maintenance |
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