Publication: Publication Date: 1 July 2019
Review of Wayside Detection and Monitoring Technologies and Their Future for North American Railroad Applications
The current prevailing technique for inspecting railcars is periodic visual examination while a train is stopped in a rail yard. In North America, such inspection is regulated by the Association of American Railroads (AAR) and the US Department of Transportation (USDOT) Federal Railroad Administration (FRA). Near real-time automated inspection of in-service freight cars would provide a way to digitally trend component health, reduce operator subjectivity, and increase the rate at which potentially failing components can be identified. Specifically facilitating condition trending, machine vision inspections provide a path for proactive maintenance rather than reactive repair. This article is aimed to introduce Materials Evaluation readers to recent developments and accomplishments with automated inspection of freight car components in North America, existing US governmental regulations, current industry challenges, and future opportunities for the automated condition assessment of freight cars.
References
AAR, 2014, “North American Freight Rail Industry,” AAR Publication, Washington, DC.
AAR, 2017, “AAR Railroad Facts,” Policy and Economic Department, Association of American Railroads, Washington, DC.
Asplund, M., M. Palo, M., S. Famurewa, and M. Rantatalo, 2014, “A Study of Railway Wheel Profile Parameters Used as Indicators of an Increased Risk of Wheel Defects,” Proceedings of the Institution of Mechanical Engineers - Part F: Journal of Rail and Rapid Transit, Vol. 230, No. 4, pp. 323–334.
Anderson, G., J. Cline, and R. Smith, 1998, “Acoustic Wayside Identification of Roller-Bearing Defects,” AAR/TTCI Technology Digest, TD-98-029, Pueblo, CO.
Barke, D., and W.K. Chiu, 2005, “Structural Health Monitoring in the Railway Industry: A Review,” Structural Health Monitoring, Vol. 4, No. 1, pp. 81–93.
Brickle, B., R. Morgan, E. Smith, J. Brosseau, and C. Pinney, 2008, “Identification of Existing and New Technologies for Wheelset Condition Monitoring,” RSSB Report -T607, Rail Safety and Standards Board, London, UK.
Chapman, S., H. Tournay, and M. Griffin, 2007, “Evaluating Repair Effectiveness via Hunting Detector Data,” AAR/TTCI Technology Digest, TD-07-033, Pueblo, CO.
Choe, H.C., Y. Wan, and A.K. Chan, 2008, “Neural Pattern Identification of Railroad Wheel-bearing Faults from Audible Acoustic Signals: Comparison of FFT, CWT, and DWT Features,” Proceedings of SPIE, Vol. 3078, pp. 480–496.
Cummings, S., H. Tournay, and K. Gonzales, 2008, “Wayside Wheel Temperature Detector Test,” AAR/TTCI Technology Digest, TD-08-013, Pueblo, CO.
Edwards, J.R., J.M. Hart, S. Todorovic, C.P.L. Barkan, N. Ahuja, Z.-Z. Chua, N. Kocher, and J. Zeman, 2007, “Development of Machine Vision Technology for Railcar Safety Appliance Inspection,” Proceedings 9th International Heavy Haul Conference, Kiruna, Sweden pp. 745–752.
FRA, 2018, “Accident Trends – Summary Statistics,” Federal Railroad Administration, Office of Safety Analysis, available at https://safetydata.fra.dot.gov/officeofsafety/publicsite/summary.aspx.
Gage, S., J. Robeda, and R. Morgan, 2001, “Evaluation of AEAT Wheel Profile Measurement System,” AAR/TTCI Technology Digest, TD01-026, Pueblo, CO.
Garcia, G., S. Kalay, and D. Carter, 2007, “Automated Crack Wheel Detection System Overview,” AAR/TTCI Technology Digest, TD-07-028, Pueblo, CO.
Garip, G., I. Üstoglu, T.V. Mumcu, Ö.T. Kaymakçi, and J. Börcsök, 2014, “Hot Box Detection System Design for Railway Vehicle Safety,” Recent Advances in Financial Planning and Product Development, pp. 31–36.
Hart, J. M., N. Ahuja, C.P.L. Barkan, and D.D. Davis, 2004, “Machine Vision System for Monitoring Railcar Health: Preliminary Results,” AAR/TTCI Technology Digest, TD-04-008, Pueblo, CO.
Hart, J. M., E. Resendiz, B. Freid, S. Sawadisavi, C.P.L. Barkan, and N. Ahuja, 2008, “Machine Vision Using Multi-Spectral Imaging for Undercarriage Inspection of Railroad Equipment,” Proceedings 8th World Congress on Railway Research, Seoul, Korea.
Johansson, A., and Nielsen, J., 2001, “Railway Wheel Out-of-Roundness – Influence of Wheel-Rail Contact Forces and Track Response,” Proceedings 13th International Wheelset Conference, Rome, Italy.
Kalay, S., A. Tajaddini, and D.H. Stone, 1992, “Detecting Wheel Tread Surface Anomalies,” ASME Rail Transportation Division, Vol. 5, pp. 165–174.
Kalay, S., 1993, “Wheel Impact Load Detector Tests and Development of Wheel-Flat Specification,” AAR Research Report R-829, AAR/CTC, Chicago, IL.
Lai, Y.-C., J.M. Hart, P. Vemuru, J. Drapa, N. Ajuha, C.P.L Barkan, L. Milhorn, and M. Stehly, 2005, “Machine Vision Analysis of the Energy Efficiency of Intermodal Trains,” Proceedings 8th International Heavy Haul Conference, Rio de Janeiro, Brazil, pp. 387–394.
Lonsdale, C., J. Pilch, and S. Dedmon, 2004, “Stress Effects of Wheel Impact Loads,” Proceedings of the 14th International Wheelset Congress, Orlando, FL, pp. 6–12, University of Illinois Press, Champaign, IL.
Moynihan, T.W., and G.W. English, 2007, “Railway Safety Technologies,” Canada Research and Traffic Group, Kingston, ON, Canada.
Mulligan, K.R., S. de Blois, A. Aronian, and R. Campbell, 2017, “Condition Based Maintenance of Railcar Roller Bearings using Predictive Wayside Alerts Based on Acoustic Bearing Detector Measurements,” Proceedings of the 11th International Heavy Haul Conference, 2–6 September, Cape Town, South Africa, International Heavy Haul Association, Virginia Beach, VA.
Poudel, A., and M. Witte, 2018, “Tycho Automated Cracked Wheel Detection System,” AAR/TTCI R-report, R-1024, Pueblo, CO.
Robeda, J., S. Gage, and R. Morgan, 2002, “Evaluation Results for WheelSpec™ Wheel Profile Measurement System,” AAR/TTCI Technology Digest, TD-02-018, Pueblo, CO.
Robeda, J., R. Morgan, and S. Gage, 2004, “Evaluation of the LynxRail Machine Vision Wheel Measurement System,” AAR/TTCI Technology Digest, TD-04-018, Pueblo, CO.
Robeda, J., D. Sammon, and B. Madrill, 2013, “Using Wheel Temperature Detector Technology to Monitor Railcar Brake System Effectiveness,” USDOT/FRA/ORD-13/50, Washington, DC.
Schlake, B.W., J. Riley, J.M. Hart, C.P.L. Barkan, S. Todorovic, and N. Ahuja, 2009, “Automated Inspection of Railcar Underbody Structural Components Using Machine Vision Technology,” Proceedings of 88th Annual TRB Meeting, 11–15 January, Washington, DC, Transportation Research Board, Washington, DC.
Southern, C., 2007, “The Successful Use of Non-Contact Acoustic Bearing Condition Monitoring,” Proceedings of the Condition Monitoring National Forum, Sydney, Australia.
Steets, P.G., and Y.H. Tse, 1998, “Conrail’s Integrated Automated Wayside Inspection,” Proceedings of ASME/IEEE Joint Railroad Conference, 16 April, Philadelphia, PA, IEEE, New York, NY.
Stratman, B., Y. Liu, and S. Mahadevan, 2007, “Structural Health Monitoring of Railroad Wheels Using Wheel Impact Load Detectors,” Journal of Failure Analysis and Prevention, Vol. 7, No. 3, pp. 218–225.
Tournay, H., S. Chapman, and R. Walker, 2006, “Evaluation of Cars Registering Salient Hunting Indices At or Above 0.25,” AAR/TTCI Technology Digest TD-06-025, Pueblo, CO.
Tournay, H., R. Lang, and S. Cummings, 2007a, “Inspection and Maintenance of Poorly Performing Cars Identified by Truck Performance Detectors,” AAR/TTCI Technology Digest TD-07-006, Pueblo, CO.
Tournay, H., S. Chapman, S. Keegan, and R. Blank, 2007b, “Initial Performance Limits: Three Hunting Detector Types,” AAR/TTCI Technology Digest TD-07-034, Pueblo, CO.
US DOT, 2011, Code of Federal Regulation (CFR) Title 49, Railroad Freight Car Safety Standards – Part 215, US Department of Transportation (USDOT), Federal Railroad Administration (FRA), Office of Safety, Washington, DC.
Wang, J., G. Anderson, J. Cline, and R. Smith, 1996, “A New Detection Technique to Identify Defective Railroad Bearings,” AAR/TTCI Technology Digest TD-96-004, Pueblo, CO.
Witte, M., A. Meddah, and B. GeMeiner, 2017, “Accelerating Machine Vision Inspection Algorithm Development,” AAR/TTCI Technology Digest TD-17-017, Pueblo, CO.
Wolf, G.P., and F.J. Peterson, 1998, “Application and Validation of the Automated Truck Performance Measurement System,” Proceedings of IEEE/ASME Joint Railroad Conference, 16 April, Philadelphia, PA, pp. 127–134, IEEE, New York, NY.
Metrics
| Usage | Shares |
|---|---|
Total Views 313 Page Views |
Total Shares 0 Tweets |
313 0 PDF Downloads |
0 0 Facebook Shares |
| Total Usage | |
| 313 |