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D.  \nCharles Vranek Professor  \nElectrical and Computer Engineering Department University of Nebraska-Lincoln  \nMichael Hempel, Ph. D.  \nResearch Assistant Professor  \nElectrical and Computer Engineering Department University of Nebraska-Lincoln  \nA Report on Research Sponsored by  \nUniversity Transportation Center for Railway Safety (UTCRS) University of Nebraska-Lincoln  \nAugust 2018  \nThe University of Texas Rio Grande Valley / 1201 West University Drive / ENGR Portable 1.100 / Edinburg, Texas 78539-2999  \nPhone: +1 (956) 665-8878 / Fax: +1 (956) [665-8879 / ](665-8879 / railwaysafety@utrgv.edu / railwaysafety.utrgv.edu)[railwaysafety@utrgv.edu / railwaysafety.utrgv.edu](665-8879 / railwaysafety@utrgv.edu / railwaysafety.utrgv.edu)  \nTECHNICAL REPORT DOCUMENTATION PAGE  \n\n| 1. Report No.\u003Cbr>26-1121-0018-010 | 2. Government Accession No. |  |  | 3. Recipient’s Catalog No. |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| 4. Title and Subtitle\u003Cbr>Unifying Railcar Monitoring Sensor Data, Maintenance Records, and Railcar Usage Information through Big Data Processing for Optimizing Railcar Maintenance and Safety |  |  |  | 5. Report Date\u003Cbr>August 2018 |  |  |\n|  |  |  |  | 6. Performing Organization Code |  |  |\n| 7. Author(s)\u003Cbr>Hamid Sharif, Ph.D.\u003Cbr>Michael Hempel, Ph.D. |  |  |  | 8. Performing Organization Report No.\u003Cbr>26-1121-0018-010 |  |  |\n| 9. Performing Organization Name and Address\u003Cbr>Nebraska Transportation Center\u003Cbr>University of Nebraska-Lincoln\u003Cbr>Prem S. Paul Research Center at Whittier School\u003Cbr>2200 Vine Street\u003Cbr>Lincoln, NE 68583-0853 |  |  |  | 10. Work Unit No. |  |  |\n|  |  |  |  | 11. Contract or Grant No.\u003Cbr>DTRT13-G-UTC59 |  |  |\n| 12. Sponsoring Agency Name and Address\u003Cbr>University Transportation Center for Railway Safety\u003Cbr>The University of Texas Rio Grande Valley\u003Cbr>1201 W University Drive\u003Cbr>Edinburg, Texas 78539 |  |  |  | 13. Type of Report and Period Covered\u003Cbr>Final Report (October 2016 – June 2018) |  |  |\n|  |  |  |  | 14. Sponsoring Agency Code |  |  |\n| 15. Supplementary Notes\u003Cbr>Conducted in cooperation with the U.S. Department of Transportation, Federal Highway Administration. |  |  |  |  |  |  |\n| 16. Abstract\u003Cbr>With this project, we investigated the use of Big Data Analytics to make rail transportation safer, by preventing derailments due to equipment failure. Railroads typically schedule railcar maintenance on best-practice intervals, which may not include the plethora of information available from their maintenance logs, track data, sensors information, bills of lading, manufacturer history, etc. This project explored the use of this data to adapt maintenance scheduling to reduce cost and increase safety. We showed the great potential inherent in this approach. |  |  |  |  |  |  |\n| 17. Key Words\u003Cbr>Railroad, Maintenance, Big Data, scheduling, Random Forest, Rail Safety |  |  | 18. Distribution Statement\u003Cbr>No restrictions. |  |  |  |\n| 19. Security Classif. (of this report)\u003Cbr>unclassified |  | 20. Security Classif. (of this page)\u003Cbr>unclassified |  |  | 21. No. of Pages\u003Cbr>31 | 22. Price |\n\nForm DOT F 1700.7 (8-72) Reproduction of completed page authorized  \nTable of Contents  \n[Abstract .......................................................................................................................................... vi](Abstract .......................................................................................................................................... vi)  \n[Chapter 1 Introduction ...............................](Chapter 1 Introduction ...............................)...................................................................................... 1  \nChapter 2 Our Approach ............................................................................................","cbCaimrVvPZoJZcB","https://ap.wps.com/l/cbCaimrVvPZoJZcB","pdf",1483709,38,"English","# Introduction\n# Our Approach\n## Methodology\n# Implementation and Results\n## Input and Data\n## Important Features Selection\n# Data Collection and Generation\n# Data Processing\n# Summary and Conclusions\n## Summary\n## Future Work\n## Publications Resulting from Research\n# References","[{\"question\":\"What problem does the project address?\",\"answer\":\"The project addresses derailments due to equipment failure by exploring ways to improve railcar maintenance safety decisions using data-driven analytics.\"},{\"question\":\"How does the approach differ from typical maintenance scheduling?\",\"answer\":\"Railroads often rely on fixed best-practice intervals, while this project uses additional information from maintenance logs, sensors, track data, and other records to tailor scheduling.\"},{\"question\":\"What is the intended impact of using Big Data processing?\",\"answer\":\"The project targets reduced maintenance cost and increased rail safety by adapting maintenance schedules based on integrated big data signals.\"}]","Unifying Railcar Monitoring Sensor Data, Maintenance Records, and Railcar Usage Information through Big Data Processing - for Optimizing Railcar Maintenance and Safety | PDF",1789133207,13]