Unifying Railcar Monitoring Sensor Data, Maintenance Records, and Railcar Usage Information through Big Data Processing for Optimizing Railcar Maintenance and Safety
| University | University of Nebraska-Lincoln (UNL) |
| Principal Investigators | Hamid Sharif, PhD., Electrical and Computer Engineering (PI) Micheal Hempel PhD., Electrical and Computer Engineering (Co-PI) |
| PI Contact Information | PKI 200C Omaha/PKI Office (402) 554-3628 hsharif@unl.edu |
| Funding Source(s) and Amounts Provided (by each agency or organization) | UTCRS (USDOT UTC Program): $94,808 Union Pacific Railroad: $100,000 Advanced TEL Lab: $22,000 |
| Total Project Cost | $216,808 |
| Agency ID or Contract Number | DTRT13-G-UTC59 |
| Start and End Dates | October 2016 - June 2018 |
| Brief Description of Research Project | 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. |
| Keywords | big data, data fusion, failure prediction, maintenance schedule optimization, machine learning, derailment prevention |
| Describe Implementation of Research Outcomes (or why not implemented) Place Any Photos Here | See Report. |
| Impacts/Benefits of Implementation (actual, not anticipated) | See Report. |
| Report | http://www.utrgv.edu/railwaysafety/_files/documents/research/operations/utcrs_sharif_unifying-railcar-monitoring-sensor-data_final-report.pdf |
| Project Website | http://www.utrgv.edu/railwaysafety/research/operations/rail-equipment-safety/index.htm |