Wheel Defect Detection With Machine Learning
Abstract
Wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate. We propose two machine learning methods to automatically detect these wheel defects, based on the wheel vertical force measured by a permanently installed sensor system on the railway network. Our methods automatically learn different types of wheel defects and predict during normal operation if a wheel has a defect or not. The first method is based on novel features for classifying time series data and it is used for classification with a support vector machine. To evaluate the performance of our method we construct multiple data sets for the following defect types: flat spot, shelling, and non-roundness. We outperform classical defect detection methods for flat spots and demonstrate prediction for the other two defect types for the first time. Motivated by the recent success of artificial neural networks for image classification, we train custom artificial neural networks with convolutional layers on 2-D representations of the measurement time series. The neural network approach improves the performance on wheels with flat spots and non-roundness by explicitly modeling the multi sensor structure of the measurement system through multiple instance learning and shift invariant networks.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Traditional machine learning-based defect detection methods have low detection accuracy and cannot meet the needs of industry.
“However, the above traditional machine learning-based defect detection methods have low detection accuracy and cannot meet the needs of industry.”
From A deep learning model for steel surface defect detection (2024) · cites this paper as [4]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
(2014) · Cited by 33,419 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
A Wavelet Tour of Signal Processing
Mallat, S. (1999) · Elsevier eBooks · doi:10.1016/b978-0-12-466606-1.x5000-4 · Cited by 16,383 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
(2013) · Cited by 3,740 · see citing works
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480 · see citing works
Solving the multiple instance problem with axis-parallel rectangles
Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00034-3 · Cited by 2,866 · see citing works
The JPEG 2000 still image compression standard
Skodras, A. N., Christopoulos, C. A., & Ebrahimi, T. (2001) · IEEE Signal Processing Magazine · doi:10.1109/79.952804 · Cited by 1,814 · see citing works
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,785 · see citing works
Acoustic Modeling Using Deep Belief Networks
Mohamed, A., Dahl, G. E., & Hinton, G. E. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2109382 · Cited by 1,731 · see citing works
Wavelet analysis for geophysical applications
Kumar, P., & Foufoula‐Georgiou, E. (1997) · Reviews of Geophysics · doi:10.1029/97rg00427 · Cited by 993 · see citing works
A review of wavelets in biomedical applications
Unser, M. A., & Aldroubi, A. (1996) · Proceedings of the IEEE · doi:10.1109/5.488704 · Cited by 759 · see citing works
Deep Multitask Learning for Railway Track Inspection
Gibert, X., Patel, V. M., & Chellappa, R. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2568758 · Cited by 444 · see citing works
Wang, Z., & Oates, T. (2015) · National Conference on Artificial Intelligence · Cited by 382 · see citing works
Out-of-round railway wheels-a literature survey
Nielsen, J. C. O., & Johansson, A. T. (2000) · Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · doi:10.1243/0954409001531351 · Cited by 276 · see citing works
Cuturi, M. (2011) · Cited by 270 · see citing works
A Kernel for Time Series Based on Global Alignments
Cuturi, M., Vert, J., Birkenes, Ø., & Matsui, T. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366260 · Cited by 240 · see citing works
Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection
Li, Y., Trinh, H. D., Haas, N., Otto, C., & Pankanti, S. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2287155 · Cited by 157 · see citing works
Performance enhancement of a multiple model adaptive estimator
Maybeck, P. S., & Hanlon, P. D. (1995) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/7.464348 · Cited by 126 · see citing works
Gooch, D. J. W. (2007) · Springer eBooks · doi:10.1007/978-0-387-30160-0_6196 · Cited by 115 · see citing works
Nielsen, J. C. O. (2009) · Elsevier eBooks · doi:10.1533/9781845696788.1.245 · Cited by 103 · see citing works
Imani, M., & Braga-Neto, U. (2015) · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/acssc.2015.7421162 · Cited by 32 · see citing works
Sensor system of detecting defects in wheels of railway vehicles running at operational speed
Nenov, N. G., Dimitrov, E. N., Vasilev, V. E., & Piskulev, P. (2011) · International Spring Seminar on Electronics Technology · doi:10.1109/isse.2011.6053953 · Cited by 12 · see citing works
Signature analysis on wheel-rail interaction for rail defect detection
Ho, T. K., Liu, S. Y., Ho, Y. T., Ho, K. H., Wong, K. K., Lee, K. Y., Tarn, H. Y., & Ho, S. L. (2008) · IET International Conference on Railway Condition Monitoring (RCM) · doi:10.1049/ic:20080342 · Cited by 12 · see citing works
Ellipsoidal Multiple Instance Learning
Krummenacher, G., Ong, C. S., & Buhmann, J. M. (2013) · Cited by 12 · see citing works
Self-citationApplication of wavelet transform to defect detection of wheelflats of railway wheels
Jianhai, Y., Qiu, Z., & Bo-shi, C. (2003) · doi:10.1109/icosp.2002.1180975 · Cited by 11 · see citing works
Huber, P., Nélain, B., & Müller, R. (2014) · Notes on numerical fluid mechanics and multidisciplinary design · doi:10.1007/978-3-662-44832-8_62 · Cited by 7 · see citing works
Anomalous tie plate detection for railroad inspection
Li, Y., & Pankanti, S. (2012) · Cited by 6 · see citing works
Formalizing Expert Knowledge Through Machine Learning
Idé, T. (2016) · Service science: research and innovations in the service economy · doi:10.1007/978-1-4939-3594-9_11 · Cited by 6 · see citing works
Deep Multi-task Learning for Railway Track InspectionPreprint
Gibert, X., Patel, V. M., & Chellappa, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.05267 · Cited by 4 · see citing works
Verheijen, E. N. G., & Elbers, F. (2014) · Notes on numerical fluid mechanics and multidisciplinary design · doi:10.1007/978-3-662-44832-8_10 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103678 · Cited by 549 · see citing works
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020) · Materials · doi:10.3390/ma13245755 · Cited by 515 · see citing works
Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD
Li, Y., Huang, H., Xie, Q., Yao, L., & Chen, Q. (2018) · Applied Sciences · doi:10.3390/app8091678 · Cited by 325 · see citing works
A literature review of Artificial Intelligence applications in railway systems
Tang, R., De Donato, L., Bes̆inović, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103679 · Cited by 324 · see citing works
A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence
Gao, Y., Li, X., Wang, X. V., Wang, L., & Gao, L. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.008 · Cited by 261 · see citing works
A bearing fault diagnosis model based on CNN with wide convolution kernels
Song, X., Cong, Y., Song, Y., Chen, Y., & Pan, L. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03177-x · Cited by 191 · see citing works
Deep learning methods for object detection in smart manufacturing: A survey
Ahmad, H. M., & Rahimi, A. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.011 · Cited by 188 · see citing works
Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning
Yang, J., Li, S., Wang, Z., & Yang, G. (2019) · IEEE Access · doi:10.1109/access.2019.2925561 · Cited by 145 · see citing works
Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications
Bešinović, N., Donato, L. D., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Marrone, S., Nardone, R., Tang, T., & Vittorini, V. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3131637 · Cited by 127 · see citing works
Liu, S., Wang, Q., & Luo, Y. (2019) · Transportation Safety and Environment · doi:10.1093/tse/tdz007 · Cited by 122 · see citing works
A deep learning model for steel surface defect detection
Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122 · see citing works
Detection of a casting defect tracked by deep convolution neural network
Lin, J., Yao, Y., Ma, L., & Wang, Y. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-1894-0 · Cited by 96 · see citing works
Kumar, A., & Harsha, S. P. (2024) · International Journal of Transportation Science and Technology · doi:10.1016/j.ijtst.2024.06.006 · Cited by 96 · see citing works
Predictive Maintenance for Railway Domain: A Systematic Literature Review
Binder, M., Mezhuyev, V., & Tschandl, M. (2023) · IEEE Engineering Management Review · doi:10.1109/emr.2023.3262282 · Cited by 87 · see citing works
Chen, S., Wang, K., Chang, C., Xie, B., & Zhai, W. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2021.115963 · Cited by 86 · see citing works
LDD-Net: Lightweight printed circuit board defect detection network fusing multi-scale features
Zhang, L., Chen, J., Chen, J., Wen, Z., & Zhou, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107628 · Cited by 82 · see citing works
Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN
Sun, X., Gu, J., Huang, R., Zou, R., & Girón-Palomares, B. (2019) · Electronics · doi:10.3390/electronics8050481 · Cited by 76 · see citing works
Learning From Accidents: Machine Learning for Safety at Railway Stations
Alawad, H., Kaewunruen, S., & An, M. (2019) · IEEE Access · doi:10.1109/access.2019.2962072 · Cited by 75 · see citing works
Bai, Y., Yang, J., Wang, J., Zhao, Y. Y., & Qiang, L. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107421 · Cited by 75 · see citing works
Prediction of Ocean Wave Height Suitable for Ship Autopilot
Lou, R., Wang, W., Li, X., Zheng, Y., & Lv, Z. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3067040 · Cited by 73 · see citing works
A Multilevel Information Fusion-Based Deep Learning Method for Vision-Based Defect Recognition
Gao, Y., Gao, L., Li, X., & Wang, X. V. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2947800 · Cited by 72 · see citing works
An approach for wheel flat detection of railway train wheels using envelope spectrum analysis
Mosleh, A., Montenegro, P. A., Costa, P. A., & Calçada, R. (2020) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2020.1832536 · Cited by 72 · see citing works
The usefulness of artificial intelligence for safety assessment of different transport modes
Tselentis, D. I., Papadimitriou, E., & van Gelder, P. H. A. J. M. (2023) · Accident Analysis & Prevention · doi:10.1016/j.aap.2023.107034 · Cited by 79 · see citing works
Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements
Wei, Z., Núñez, A. A., Li, Z., & Dollevoet, R. P. B. J. (2017) · Sensors · doi:10.3390/s17102236 · Cited by 70 · see citing works
Garcia, G. R., Michau, G., Ducoffe, M., Gupta, J. S., & Fink, O. (2021) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x21994446 · Cited by 69 · see citing works
Wang, W., Sun, Q., Zhao, Z., Fang, Z., Tay, J. S., See, K. Y., & Zheng, Y. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3250766 · Cited by 67 · see citing works
Ni, Y., & Zhang, Q.-H. (2020) · Structural Health Monitoring · doi:10.1177/1475921720921772 · Cited by 64 · see citing works
Early wheel flat detection: an automatic data-driven wavelet-based approach for railways
Mosleh, A., Meixedo, A., Ribeiro, D. R. F., Montenegro, P. A., & Calçada, R. (2022) · Vehicle System Dynamics · doi:10.1080/00423114.2022.2103436 · Cited by 62 · see citing works
Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
Liu, Y., Xu, K., & Xu, J. (2019) · Applied Sciences · doi:10.3390/app9153127 · Cited by 58 · see citing works
State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review
Shaikh, M. Z., Ahmed, Z. A., Chowdhry, B. S., Baro, E. N., Hussain, T., Uqaili, M. A., Mehran, S., Kumar, D., & Shah, A. A. (2023) · IEEE Access · doi:10.1109/access.2023.3240167 · Cited by 57 · see citing works
Enhanced Few-Shot Learning for Intrusion Detection in Railway Video Surveillance
Gong, X. F., Chen, X., Zhong, Z. D., & Chen, W. R. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3102613 · Cited by 56 · see citing works
The Automatic Rail Surface Multi-Flaw Identification Based on a Deep Learning Powered Framework
Li, Z., Qi, H., & Zhang, Z. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3109949 · Cited by 55 · see citing works
A Review of Deep Learning Applications for Railway Safety
Oh, K. T., Yoo, M., Jin, N., Ko, J., Seo, J., Joo, H., & Ko, M. (2022) · Applied Sciences · doi:10.3390/app122010572 · Cited by 55 · see citing works
Fast classification for rail defect depths using a hybrid intelligent method
Jiang, Y., Wang, H., Tian, G. Y., Yi, Q., Zhao, J., & Zhen, K. (2018) · Optik · doi:10.1016/j.ijleo.2018.11.053 · Cited by 55 · see citing works
Wan, T. H., Tsang, C. W., Hui, K., & Chung, E. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106037 · Cited by 54 · see citing works
Phusakulkajorn, W., Núñez, A. A., Wang, H., Jamshidi, A. A., Zoeteman, A., Ripke, B., Dollevoet, R. P. B. J., Schutter, B. D., & Li, Z. (2023) · Intelligent Transportation Infrastructure · doi:10.1093/iti/liad016 · Cited by 54 · see citing works
Feature learning for fault detection in high-dimensional condition monitoring signals
Michau, G., Hu, Y., Palmé, T., & Fink, O. (2019) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x19868335 · Cited by 54 · see citing works
Rosa, A. D., Kulkarni, R., Qazizadeh, A., Berg, M., Gialleonardo, E. D., Facchinetti, A., & Bruni, S. (2020) · Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · doi:10.1177/0954409720906649 · Cited by 54 · see citing works
Guo, S., Feng, H., Feng, W., Lv, G., Chen, D., Liu, Y., & Wu, X. (2021) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2021.3087949 · Cited by 51 · see citing works
A novel texture-based damage detection method for wire ropes
Zhou, P., Zhou, G., He, Z., Tang, C., Zhu, Z., & Li, W. (2019) · Measurement · doi:10.1016/j.measurement.2019.106954 · Cited by 51 · see citing works
Defect Detection of Rail Surface with Deep Convolutional Neural Networks
Liang, Z., Zhang, H., Liu, L., He, Z., & Zheng, K. (2018) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2018.8630525 · Cited by 49 · see citing works
Chuma, E. L., & Iano, Y. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3028494 · Cited by 49 · see citing works
Kapoor, R., Goel, R., & Sharma, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12059-z · Cited by 47 · see citing works
Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning
Sresakoolchai, J., & Kaewunruen, S. (2021) · Vibration · doi:10.3390/vibration4020022 · Cited by 47 · see citing works
Fault diagnosis of railway wheelsets: A review
Ye, Y., Li, H., Wang, Q., Li, F., Yi, C., Peng, X., Huang, C., & Zeng, J. (2024) · Measurement · doi:10.1016/j.measurement.2024.116169 · Cited by 46 · see citing works
Bao, W., Ren, Y., Wang, N., Hu, G., & Yang, X. (2021) · Remote Sensing · doi:10.3390/rs13204134 · Cited by 45 · see citing works
Khan, M. N., & Ahmed, M. M. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105521 · Cited by 43 · see citing works
Deep Generative Model Using Unregularized Score for Anomaly Detection With Heterogeneous Complexity
Matsubara, T., Sato, K., Hama, K., Tachibana, R., & Uehara, K. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3027724 · Cited by 43 · see citing works
Methods for fault diagnosis of high-speed railways: A review
Zang, Y., Shangguan, W., Cai, B., Wang, H., & PECHT, M. G. (2019) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x18823932 · Cited by 42 · see citing works
Abtane, M., Dahi, K., Martinez, H., Mohamed, S., Kimi, H. E., Dahhassi, C., Hlib, C., & Borges, L. F. (2025) · Measurement · doi:10.1016/j.measurement.2025.117098 · Cited by 44 · see citing works
Semantic Segmentation of Metal Surface Defects and Corresponding Strategies
Zhang, Z., Wang, W., & Tian, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3282301 · Cited by 40 · see citing works
Categorical Feature GAN for Imbalanced Intelligent Fault Diagnosis of Rotating Machinery
Dai, J., Wang, J., Yao, L., Huang, W., & Zhu, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3298425 · Cited by 40 · see citing works
Bai, Y., Yang, J., Wang, J., & Li, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3000068 · Cited by 40 · see citing works
A Survey on Automatic Inspections of Overhead Contact Lines by Computer Vision
Yu, L., Gao, S., Zhang, D., Kang, G., Zhan, D., & Roberts, C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3119023 · Cited by 41 · see citing works
Wheel hub defect detection based on the DS-Cascade RCNN
Cheng, S., Lu, J., Yang, M., Zhang, S., Xu, Y., Zhang, D., & Wang, H. (2022) · Measurement · doi:10.1016/j.measurement.2022.112208 · Cited by 40 · see citing works
An Unsupervised Learning Approach for Wayside Train Wheel Flat Detection
Mohammadi, M., Mosleh, A., Vale, C., Ribeiro, D. R. F., Montenegro, P. A., & Meixedo, A. (2023) · Sensors · doi:10.3390/s23041910 · Cited by 37 · see citing works
Rombach, K., Michau, G., & Fink, O. (2021) · Sensors · doi:10.3390/s21103550 · Cited by 36 · see citing works
Resource-Aware Time Series Imaging Classification for Wireless Link Layer Anomalies
Bertalanič, B., Meža, M., & Fortuna, C. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3149091 · Cited by 37 · see citing works
YOLOv8-QR: An improved YOLOv8 model via attention mechanism for object detection of QR code defects
Zhao, L., Liu, J., Ren, Y., Lin, C., Liu, J., Abbas, Z., Islam, M. S., & Xiao, G. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109376 · Cited by 36 · see citing works
Costa, M. D. A., Braga, J. A. P., & Andrade, A. R. (2020) · Quality and Reliability Engineering International · doi:10.1002/qre.2729 · Cited by 35 · see citing works
Fabric defect detection based on low-rank decomposition with structural constraints
Liu, G., & Li, F. (2021) · The Visual Computer · doi:10.1007/s00371-020-02040-y · Cited by 35 · see citing works
Track Defect Detection for High-Speed Maglev Trains via Deep Learning
He, Y., Wu, J., Zheng, Y., Zhang, Y., & Hong, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3151165 · Cited by 35 · see citing works
Data-Driven Intelligent Predictive Maintenance of Industrial Assets
Fink, O. (2019) · Women in engineering and science · doi:10.1007/978-3-030-11866-2_25 · Cited by 32 · see citing works
Integration of Building Information Modeling and Machine Learning for Railway Defect Localization
Sresakoolchai, J., & Kaewunruen, S. (2021) · IEEE Access · doi:10.1109/access.2021.3135451 · Cited by 32 · see citing works
On-Site Reliable Wheel Size Measurement Based on Multisensor Data Fusion
Pan, X., Liu, Z., & Zhang, G. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2890328 · Cited by 31 · see citing works
Unified Deep Learning Architecture for the Detection of All Catenary Support Components
Liu, W., Liu, Z., Núñez, A. A., & Han, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2967831 · Cited by 31 · see citing works
A weak supervision machine vision detection method based on artificial defect simulation
Li, C., Huang, Y., Li, H., & Zhang, X. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106466 · Cited by 31 · see citing works
Adaptive Deep Learning for High-Speed Railway Catenary Swivel Clevis Defects Detection
Gao, S., Kang, G., Yu, L., Zhang, D., Wei, X., & Zhan, D. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3024216 · Cited by 30 · see citing works
Lourenço, A., Ferraz, C., Ribeiro, D. R. F., Mosleh, A., Montenegro, P. A., Vale, C., Meixedo, A., & Marreiros, G. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107433 · Cited by 30 · see citing works
Huang, H., & Chuang, C.-C. (2020) · IEEE Access · doi:10.1109/access.2020.3032715 · Cited by 28 · see citing works
Wang, J., Yang, J., Wang, Y., Bai, Y., Zhang, T., & Yao, D. (2021) · International Journal of Rail Transportation · doi:10.1080/23248378.2021.2000897 · Cited by 28 · see citing works
Wheel Defect Detection Using a Hybrid Deep Learning Approach
Shaikh, K., Kalwar, I. H., & Chowdhry, B. S. (2023) · Sensors · doi:10.3390/s23146248 · Cited by 28 · see citing works
A semi-supervised co-training model for predicting passenger flow change in expanding subways
Wang, K., Guo, B., Yang, H., Li, M., Zhang, F., & Wang, P. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118310 · Cited by 27 · see citing works
Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review
Fu, W., He, Q., Feng, Q., Li, J., Zheng, F., & Zhang, B. (2023) · Sensors · doi:10.3390/s23083916 · Cited by 26 · see citing works
Review on Rail Damage Detection Technologies for High-Speed Trains
Wang, Y., Miao, B., Zhang, Y., Huang, Z., & Xu, S. (2025) · Applied Sciences · doi:10.3390/app15147725 · Cited by 26 · see citing works
Yang, S.-L., Yang, R., Zha, F.-Y., Liu, H.-D., Liang, L.-P., & Xu, K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106649 · Cited by 26 · see citing works
A Hierarchical Features-Based Model for Freight Train Defect Inspection
Xiao, L., Wu, B., Hu, Y., & Liu, J. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2954124 · Cited by 25 · see citing works
Research on a Product Quality Monitoring Method Based on Multi Scale PP-YOLO
Li, Y., Huang, H., Chen, Q., Fan, Q., & Quan, H. (2021) · IEEE Access · doi:10.1109/access.2021.3085338 · Cited by 25 · see citing works
Wheel-flat detection on trams using envelope analysis with Hilbert transform
Nowakowski, T., Komorski, P., Szymański, G. M., & Tomaszewski, F. (2019) · Latin American Journal of Solids and Structures · doi:10.1590/1679-78255010 · Cited by 25 · see citing works
Yuan, M., Li, J., Liu, Y., & Gao, X. (2019) · Journal of Testing and Evaluation · doi:10.1520/jte20180545 · Cited by 24 · see citing works
Recent Studies on Segmentation Techniques for Food Recognition: A Survey
Chopra, M., & Purwar, A. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09598-3 · Cited by 24 · see citing works
Chen, S., Zhou, L., & Ni, Y. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.108853 · Cited by 24 · see citing works
Ye, T., Zheng, Z., Li, X., Zhao, Z., & Gao, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3288258 · Cited by 24 · see citing works
Time series data mining for railway wheel and track monitoring: a survey
Lourenço, A., Ribeiro, D. R. F., Fernandes, M., & Marreiros, G. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10138-w · Cited by 23 · see citing works
Abidi, M. H., Umer, U., Mohammed, M. K., Aboudaif, M. K., & Alkhalefah, H. (2020) · Mathematics · doi:10.3390/math8112008 · Cited by 23 · see citing works
Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
Baasch, B., Heusel, J., Roth, M., & Neumann, T. (2021) · Applied Sciences · doi:10.3390/app11041432 · Cited by 23 · see citing works
Silva, R., Guedes, A., Ribeiro, D. R. F., Vale, C., Meixedo, A., Mosleh, A., & Montenegro, P. A. (2023) · Sensors · doi:10.3390/s23031544 · Cited by 23 · see citing works
Wei, Z., Boogaard, A., Núñez, A. A., Li, Z., & Dollevoet, R. P. B. J. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2816800 · Cited by 23 · see citing works
A Low-Cost Tire Pressure Loss Detection Framework Using Machine Learning
Wei, L., Wang, X., Li, L., Yu, L., & Liu, Z. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3047040 · Cited by 22 · see citing works
Jorge, T., Magalhães, J., Silva, R., Guedes, A., Ribeiro, D. R. F., Vale, C., Meixedo, A., Mosleh, A., Montenegro, P. A., & Cury, A. (2024) · Vehicle System Dynamics · doi:10.1080/00423114.2024.2333771 · Cited by 22 · see citing works
Zhao, Y., He, X. Y., Zhou, D., & PECHT, M. G. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3036102 · Cited by 22 · see citing works
Wear of Wheels and Axle in Locomotive and Measures Taken by Indian Railway
Kumar, U., Kasvekar, R., Sharma, S. K., & Upadhyay, R. K. (2021) · Energy, environment, and sustainability · doi:10.1007/978-981-16-8337-4_5 · Cited by 21 · see citing works
Zehelein, T., Hemmert-Pottmann, T., & Lienkamp, M. (2020) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan9010008 · Cited by 21 · see citing works
An end-to-end learning method for industrial defect detection
Wu, Y., Guo, D., Liu, H., & Huang, Y. (2019) · Assembly Automation · doi:10.1108/aa-08-2018-114 · Cited by 20 · see citing works
Sun, Z., & Lu, J. (2021) · IEEE Access · doi:10.1109/access.2021.3093482 · Cited by 20 · see citing works
Wheel flat detection and severity classification using deep learning techniques
Sresakoolchai, J., & Kaewunruen, S. (2021) · Insight - Non-Destructive Testing and Condition Monitoring · doi:10.1784/insi.2021.63.7.393 · Cited by 20 · see citing works
Sresakoolchai, J., Hamarat, M. Z., & Kaewunruen, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-29292-7 · Cited by 20 · see citing works
Defective texture classification using optimized neural network structure
Mohammed, K. M. C., Kumar, S. S., & Prasad, G. S. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.04.017 · Cited by 20 · see citing works
Zeng, Y., Song, D., Zhang, W., Zhou, B., Xie, M., & Tang, X. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3093019 · Cited by 19 · see citing works
Taxonomy of Machine Learning Algorithms and Its Applications
Rani, P., Kumar, R., Jain, A., & Lamba, R. (2020) · Journal of Computational and Theoretical Nanoscience · doi:10.1166/jctn.2020.8922 · Cited by 19 · see citing works
- Authors
- Krummenacher, G., Ong, C. S., Koller, S., Kobayashi, S., & Buhmann, J. M.
- Affiliations
- ETH Zurich (CH)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2018-04
- Volume
- 19
- Issue
- 4
- Pages
- 1176-1187
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2744245146
- Cited by
- 258
- References
- 37
- Citations by year
- 2026: 15 · 2025: 32 · 2024: 35 · 2023: 38 · 2022: 28 · 2021: 42 · 2020: 35 · 2019: 22 · 2018: 9 · 2017: 2
- FWCI
- 14.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Railway Engineering and Dynamics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Transportation Systems and Safety
- Keywords
- wheel defect detection, freight cars, flat spots, shelling, time series classification, support vector machine, convolutional neural networks, multiple instance learning, railway infrastructure damage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
