Track geometry estimation from vehicle–body acceleration for high-speed railway using deep learning technique

References

• Weston P, Roberts C, Yeo G, et al. Perspectives on railway track geometry condition monitoring from in-service railway vehicles. Veh Syst Dyn. 2015;53(7):1063–1091.
   Web of Science ®Google Scholar
• Tsunashima H. Railway condition monitoring, present and application for regional railways. Tokyo: Research Institute of Industrial Technology, Nihon University; 2018.
   Google Scholar
• Karis T, Berg M, Stichel S, et al. Correlation of track irregularities and vehicle responses based on measured data. Veh Syst Dyn. 2018;56(6):967–981.
   Web of Science ®Google Scholar
• Ward C, Weston P, Stewart E, et al. Condition monitoring opportunities using vehicle-based sensors. Proc Inst Mech Eng Part F: J Rail Rapid Transit. 2011;225(2):202–218.
   Web of Science ®Google Scholar
• Weston PF, Ling CS, Goodman CJ, et al. Monitoring lateral track irregularity from in-service railway vehicles. Proc Inst Mech Eng Part F: J Rail Rapid Transit. 2007;221(1):89–100.
   Web of Science ®Google Scholar
• Weston P, Ling CS, Goodman CJ, et al. Monitoring vertical track irregularity from in-service railway vehicles. Proc Inst Mech Eng Part F: J Rail Rapid Transit. 2007;221(1):75–88.
   Web of Science ®Google Scholar
• Sun X, Yang F, Shi J, et al. On-board detection of longitudinal track irregularity via axle box acceleration in HSR. IEEE Access. 2021;9:14025–14037.
   Web of Science ®Google Scholar
• Tsunashima H, Naganuma Y, Matsumoto A, et al. Condition monitoring of railway track using in-service vehicle. In: Perpinya X, editor. Reliability and safety in railway. Rijeka: InTech; 2012. p. 334–356.
   Google Scholar
• Tsunashima H. Condition monitoring of railway tracks from car-body vibration using a machine learning technique. Appl Sci-Basel. 2019;9(13):2734.
   Web of Science ®Google Scholar
• Naganuma Y, Kobayashi T, Tsunashima H. Track geometry estimation from car-body motions of railway vehicles. J Mech Syst Transp Logistics. 2013;6(2):132–141.
   Google Scholar
• Tsunashima H, Naganuma Y, Kobayashi T. Track geometry estimation from car-body vibration. Veh Syst Dyn. 2014;52(sup1):207–219.
   Web of Science ®Google Scholar
• Odashima M, Azami S, Naganuma Y, et al. Track geometry estimation of a conventional railway from car-body acceleration measurement. Mech Eng J. 2017;4(1):16–498.
   Web of Science ®Google Scholar
• Vinberg E, Martin M, Firdaus A, et al. Railway applications of condition monitoring. Stockholm: KTH Royal Institute of Technology; 2018.
   Google Scholar
• Alfi S, Bruni S. Estimation of long wavelength track irregularity from onboard measurement. In: Proceedings of the 4th IET International Conference on Railway Condition Monitoring (RCM2008); 2008 Jun 18–20; Derby, UK.
   Google Scholar
• Muñoz S, Ros J, Urda P, et al. Estimation of lateral track irregularity through Kalman filtering techniques. IEEE Access. 2021;9:60010–60025.
   Web of Science ®Google Scholar
• Gadhave R, Vyas NS. Rail-wheel contact forces and track irregularity estimation from on-board accelerometer data. Veh Syst Dyn. 2021;1-21. https://doi.org/10.1080/00423114.2021.1899253.
   Web of Science ®Google Scholar
• He K, Zhang X, Ren S, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV); 2015 Dec 7–13; Santiago, Chile. Piscataway, NJ: IEEE; 2015. p. 1026–1034.
   Google Scholar
• Gehring J, Auli M, Grangier D, et al. Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning; 2017 Aug 6–11; Sydney, NSW, Australia. Available from: http://proceedings.mlr.press/v70/gehring17a.html.
   Google Scholar
• Cho K, Merrienboer BV, Gulcehre C, et al. Learning phrase representations using RNN Encoder-Decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP); 2014 Oct 25–29; Doha, Qatar. Stroudsburg, PA: Association for Computational Linguistics; 2014. p. 1724–1734.
   Google Scholar
• Chung J, Gulcehre C, Cho KH, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. Preprint; 2014. arXiv:1412.3555.
   Google Scholar
• Ma S, Gao L, Liu X, et al. Deep learning for track quality evaluation of high-speed railway based on vehicle-body vibration prediction. IEEE Access. 2019;7:185099–185107.
   Web of Science ®Google Scholar
• Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. Preprint; 2014. arXiv: 1409.0473.
   Google Scholar
• Xu K, Ba J, Kiros R, et al. Show, attend and tell: neural image caption generation with visual attention. Preprint; 2015. arXiv: 1502.03044.
   Google Scholar
• Chorowski J, Bahdanau D, Serdyuk D, et al. Attention-based models for speech recognition. Preprint; 2015. arXiv: 1506.07503.
   Google Scholar
• TG/GW 115-. Maintenance criteria for ballastless track of high-speed railway. Beijing: Ministry of Railways of the People’s Republic of China; 2012.
   Google Scholar
• Furukawa A. A method to predict vertical vehicle motion caused by track irregularities. Proc Inst Mech Eng Part F: J Rail Rapid Transit. 2017;231:431–443.
   Web of Science ®Google Scholar
• Ince T, Kiranyaz S, Eren L, et al. Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans Ind Electron. 2016;63(11):7067–7075.
   Web of Science ®Google Scholar
• Wu H, Chen J, Liu X, et al. One-dimensional CNN-based intelligent recognition of vibrations in pipeline monitoring with DAS. J Lightwave Technol. 2019;37(17):4359–4366.
   Web of Science ®Google Scholar
• Kawasaki J. Estimation of rail irregularities [master’s thesis]. Japan: The University of Tokyo; 2001.
   Google Scholar
• Yang Z, Yang D, Dyer C, et al. Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies; 2016 Jun 12–17; San Diego, California. Stroudsburg, PA: Association for Computational Linguistics; 2016. p. 1480–1489.
   Google Scholar
• He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, Nevada, US. Piscataway, NJ: IEEE; 2016. p. 770–778.
   Google Scholar
• Hochreiter S, Schmidhuber J. Long Short-term memory. Neural Comput. 1997;9(8):1735–1780.
   PubMed Web of Science ®Google Scholar