| dc.contributor.author | Bridgelall, Raj | |
| dc.contributor.author | Lu, Pan | |
| dc.contributor.author | Tolliver, Denver D. | |
| dc.description.abstract | Track and equipment failures dominate railroad accident causes. Railroads must visually
inspect most tracks in service as often as twice weekly to comply with the Federal Track Safety Standards.
They augment visual inspections with automated non-destructive-evaluation (NDE) equipment to locate
developing and mature defects. However, the defect formation rate is escalating with increasing traffic load
density and continuously declining railroad employment per track-mile. This indicates a widening gap
between the rate of defect formation and the resources available to find them before they result in
accidents, delays, and lost revenue. With resources thinly stretched and the rate of defect formation escalating with traffic load-density, railroads are seeking to enhance the efficiency of inspections and maintenance of way. This paper describes
the development of a Rolling-stock Automatic In-situ Line Deterioration & Operating Condition Sensing
(RAILDOCS) system to automatically locate and classify track and rail vehicle defects. The approach
incorporates a new low-cost wireless sensor technology and Cloud computing method to guide and focus
inspection activities to locations of equipment and track defect symptoms, leading to efficient diagnosis and
remediation. RAILDOCS has on-board sensors which will continuously monitor track and vehicle condition and
transmit a 3D inertial signature for a remote processor to analyze and produce a complete and updated
picture of aggregate track and equipment quality. RAILDOCS complement more expensive visual and
NDE methods by reallocating time spent on defect discovery to detailed inspections of prioritized defect
symptom locations. Symptom sensors integrate micro-electro-mechanical (MEMS), global positioning
system (GPS) satellite receivers, wireless communications, and microprocessors technology. Cloud
computing and signal processing algorithms produce a track quality index, and forecast optimum
maintenance triggers. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Rolling-Stock Automatic In-Situ Line Deterioration and Operating Condition Sensing | en_US |
| dc.type | Article | en_US |
| dc.type | Preprint | en_US |
| dc.description | Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM). | en_US |
| dc.date.accessioned | 2017-12-06T15:24:58Z | |
| dc.date.available | 2017-12-06T15:24:58Z | |
| dc.date.issued | 2013 | |
| dc.identifier.uri | https://hdl.handle.net/10365/27000 | |
| dc.subject.lcsh | Transportation. | en_US |
| dc.subject.lcsh | Railroads. | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.identifier.citation | Lu, P., Bridgelall, R., Tolliver, D., "Rolling-stock Automatic in-situ Line Deterioration and Operating Condition Sensing," Proc. 92nd Annual Meeting of the Transportation Research Board, 13-0312, Washington, DC, January 13-17, 2013. | en_US |
| dc.description.uri | https://www.ugpti.org/about/staff/viewbio.php?id=79 | |
| dc.language.iso | en_US | en_US |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.contributor.organization | Upper Great Plains Transportation Institute | |
| ndsu.college | College of Business | |
| ndsu.department | Transportation and Logistics | |
