Rolling stock, particularly of freight railroads, is currently maintained using regular preventative and corrective maintenance schedules. This maintenance approach recommends sets of inspections and maintenance procedures based on the average expected wear and tear across their inventory. In practice, however, this approach to scheduling preventative maintenance is not always effective. When scheduled too soon, it results in a loss of operating revenue, whereas when it is scheduled too late, equipment failure could lead to costly and disastrous derailments. Instead, proactive maintenance scheduling based on Big Data Analytics (BDA) could be utilized to replace traditional scheduling, resulting in optimized maintenance cycles for higher train safety, availability, and reliability. BDA could also be used to discover patterns and relationships that lead to train failures, identify manufacturer reliability concerns, and help validate the effectiveness of operational improvements. In this work, we introduce a train inventory simulation platform that enables the modelling of different train components such as wheels, brakes, axles, and bearings. The simulator accounts for the wear and tear in each component and generates a comprehensive data set suitable for BDA that can be used to evaluate the effectiveness of different BDA approaches in discerning patterns and extracting knowledge from the data. It provides the basis for showing that BDA algorithms such as Random Forest [9] and Linear Regression can be utilized to create models for proactive train maintenance scheduling. We also show the capability of BDA to detect hidden patterns and to predict failure of train components with high accuracy.
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2019 Joint Rail Conference
April 9–12, 2019
Snowbird, Utah, USA
Conference Sponsors:
- Rail Transportation Division
ISBN:
978-0-7918-5852-3
PROCEEDINGS PAPER
Big Data Analytics for Proactively Optimizing Rolling Stock Maintenance
Naji Albakay,
Naji Albakay
University of Nebraska-Lincoln, Omaha, NE
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Michael Hempel,
Michael Hempel
University of Nebraska-Lincoln, Omaha, NE
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Hamid Sharif
Hamid Sharif
University of Nebraska-Lincoln, Omaha, NE
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Naji Albakay
University of Nebraska-Lincoln, Omaha, NE
Michael Hempel
University of Nebraska-Lincoln, Omaha, NE
Hamid Sharif
University of Nebraska-Lincoln, Omaha, NE
Paper No:
JRC2019-1253, V001T06A005; 5 pages
Published Online:
July 18, 2019
Citation
Albakay, N, Hempel, M, & Sharif, H. "Big Data Analytics for Proactively Optimizing Rolling Stock Maintenance." Proceedings of the 2019 Joint Rail Conference. 2019 Joint Rail Conference. Snowbird, Utah, USA. April 9–12, 2019. V001T06A005. ASME. https://doi.org/10.1115/JRC2019-1253
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