Reference health
Developing Machine Learning-Based Models for Railway Inspection
15/15 checkable references clean · checked 2026-08-29
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
18 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 15 checked references that resolve
resolves10.1080/15732479.2020.1832536
An approach for wheel flat detection of railway train wheels using envelope spectrum analysis
resolves10.1109/ICMLA.2007.59
Model evaluation for prognostics: estimating cost saving for the end users
resolves10.1007/s10489-008-0123-1
Two-stage classifications for improving time-to-failure estimates: a case study in prognostic of train wheels
resolves10.5194/isprsannals-II-5-W2-223-2013
Rail Track Detection and Modelling in Mobile Laser Scanner Data
resolves10.1109/WACV.2012.6163021
Enhanced rail component detection and consolidation for rail track inspection
resolves10.1109/19.850395
Composite real-time image processing for railways track profile measurement
resolves10.1109/TITS.2010.2041057
Predicting Vertical Acceleration of Railway Wagons Using Regression Algorithms
resolves10.1109/TIE.2015.2389761
Improvements in Axle Box Acceleration Measurements for the Detection of Light Squats in Railway Infrastructure
resolves10.1109/IJCNN.2017.7966039
Time series classification from scratch with deep neural networks: A strong baseline
The 18 references without a DOI — listed, not checked
no DOI — not checkedSalient Systems Inc. (2020, December 21). Preventing Train Derailment. Available online: http://www.salientsystems.com.
no DOI — not checkedAccident Trends—Summary Statistics (2020, December 21). Building Energy Data Book, Department of Energy, United States, Available online: http://safetydata.fra.dot.gov/officeofsafety/publicsite/summary.aspxDOE.
no DOI — not checked(2020, December 21). How Can Rail Operators and OEMs Benefit from the Disruption Brought on by Digitization?. Available online: https://www.mckinsey.com/media/McKinsey/Industries/Public/the-rail-sectors-hanging-maintenance-game.pdf.
no DOI — not checkedKalay, S. (2011, January 15–16). Research Implementation—Improved Rail Safety & Efficiency. Proceedings of the 16th Annual AAR Research Review, Pueblo, CO, USA.
no DOI — not checkedLechowicz, S., and Hunt, C. (1999, January 3–5). Monitoring and Managing Wheel Condition and Loading. Transportation Recording: 2000 and Beyond. Proceedings of the International Symposium on Transportation Recorders, Arlington, VA, USA.
no DOI — not checkedConnell, D. (2011, January 15–16). Rail Safety and Rail Life Extension. Proceedings of the 16th Annual AAR Research Review, Pueblo, CO, USA.
no DOI — not checked(2020, December 21). ENSCO—Track Inspection Vehicles. Available online: http://www.ensco.com/userfiles/file/Products_Services_PDF/07_Rail/Track-Inspection-Systems/100059-Track-Inspection-Vehicles-ENSCO-Rail.pdf.
no DOI — not checkedResearch and Traffic Group (2007). Railway Safety Technologies, Research and Traffic Group. Technical Report for Railway Safety Act Review Secretariat.
no DOI — not checkedTransportation Safety Board of Canada (2017). Rail Way Investigation Report, Transportation Safety Board of Canada. R15H0013.
no DOI — not checkedYang, C., Tran, M., and Liu, Y. (2017, January 9–11). A Machine Learning-based Method for Rail inspection. Proceedings of the 2017 International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2017), Nagoya, Japan.
no DOI — not checkedBerry, A., Nejikovsky, B., Gibert, X., and Tajaddini, A. (2008, January 18–22). High speed video inspection of joint bars using advanced image collection and processing techniques. Proceedings of the 8th World Congres on Railway Research, Seoul, Korea.
no DOI — not checkedThe stationary wavelet transform and some statistical applications
no DOI — not checkedLiu, C.L. (2020, December 21). A Tutorial of the Wavelet Transform. Available online: http://disp.ee.ntu.edu.tw/tutorial/WaveletTutorial.
no DOI — not checkedHatami, N., Gavet, Y., and Debayle, J. (2017, January 13–15). Classification of Time-Series Images Using Deep Convolutional Neural Networks. Proceedings of the Tenth International Conference on Machine Vision 2017, Vienna, Austria.
no DOI — not checkedKrizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA.
no DOI — not checkedHe, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
no DOI — not checkedJones, E., Oliphant, E., and Peterson, P. (2020, December 21). SciPy: Open Source Scientific Tools for Python. Available online: http://www.scipy.org/.
no DOI — not checkedFRA (Federal Railroad Administration) (2015). Track Inspector Rail Defect Reference Manual.
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