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Abstract From 2013 to 2022, 1671 derailments have been reported by the Federal Railroad Administration (FRA), 8.2% of which were due to journal bearing defects. The University Transportation Center for Railway Safety (UTCRS) designed an onboard monitoring system that tracks vibration waveforms over time to assess bearing health through three analysis levels. However, the speed of the bearing, a fundamental parameter for these analyses, is often acquired from Global Positioning System (GPS) data, which is typically not available at the sensor location. To solve this issue, this paper proposes to employ Machine Learning (ML) algorithms to extract the speed and other essential features from existing vibration data, eliminating the need for additional speed sensors. Specifically, the proposed method tries to extract the speed information from the signatures that are embedded in the Power Spectral Density (PSD) plot, which enables rapid real-time analysis of bearings while the train is in motion. The rapid extraction of data could be sent to a cloud accessible by train dispatchers and railcar owners for assessment of bearings and scheduling of replacements before defects reach a dangerous size. Eventually, the developed algorithm will reduce derailments and unplanned field replacements and afford rail stakeholders more cost-effective preventive maintenance.
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A Customized Hybrid Approach to Infrastructure Maintenance Scheduling in Railroad Networks under Variable Productivities
Abstract Railroads are maintained routinely by using various types of rail‐bound machines so as to achieve the longest possible rail life and reduce the safety risks associated with unanticipated rail failures. The rail maintenance routing and scheduling problem (RMRSP), which involves routing of multiple maintenance vehicles and scheduling of hundreds of maintenance jobs over a large‐scale network, is usually subject to various types of complex constraints and extremely difficult to solve. This article proposes a vehicle routing problem with time windows (VRPTW) formulation for RMRSP and develops a customized stepwise algorithm to solve the problem. A series of numerical experiments are conducted to demonstrate that the proposed algorithm works very effectively, significantly outperforming the state‐of‐the‐art commercial solver. The results of two real‐world instances from a Class I railroad company show that the proposed model and solution algorithm enable the expensive maintenance vehicles to achieve a higher level of utilization, that is, spending more time on working and less time on deadhead traveling.
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- Award ID(s):
- 1662825
- PAR ID:
- 10070520
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Computer-Aided Civil and Infrastructure Engineering
- Volume:
- 33
- Issue:
- 10
- ISSN:
- 1093-9687
- Page Range / eLocation ID:
- p. 815-832
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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