Multiple damage detection of maglev rail joints using time-frequency spectrogram and convolutional neural network |
Wang, Su-Mei
(Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
Jiang, Gao-Feng (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) Ni, Yi-Qing (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) Lu, Yang (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) Lin, Guo-Bin (Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University) Pan, Hong-Liang (Maglev Transportation Engineering R&D Center, Tongji University) Xu, Jun-Qi (Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University) Hao, Shuo (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) |
1 | Lin, Y.Z., Nie, Z.H. and Ma, H.W. (2017), "Structural damage detection with automatic feature-extraction through deep learning", Comput.-Aided Civil Infrastr. Eng., 32(12), 1025-1046. https://doi.org/10.1111/mice.12313 DOI |
2 | Masada, E. (1993), "Development of maglev transportation in Japan: present state and future prospects", Proceedings of 13th International Conference on Magnetically Levitated Systems and Linear Drives, Argonne, IL, USA, May. |
3 | Oregui, M., Li, S., Nunez, A., Li, Z., Carroll, R. and Dollevoet, R. (2017), "Monitoring bolt tightness of rail joints using axle box acceleration measurements", Struct. Control Health Monitor., 24(2), e1848. https://doi.org/10.1002/stc.1848 DOI |
4 | Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014), "Dropout: a simple way to prevent neural networks from overfitting", J. Mach. Learn. Res., 15(1), 1929-1958. |
5 | Verstraete, D., Ferrada, A., Droguett, E.L., Meruane, V. and Modarres, M. (2017), "Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings", Shock Vib., 5067651. https://doi.org/10.1155/2017/5067651 DOI |
6 | Zhang, Y., Sun, X.W., Loh, K.J., Su, W.S., Xue, Z.G. and Zhao, X.F. (2020), "Autonomous bolt loosening detection using deep learning", Struct. Health Monitor., 19(1), 105-122. https://doi.org/10.1177/1475921719837509 DOI |
7 | Zhong, K.F., Teng, S., Liu, G., Chen, G.F. and Cui, F.S. (2020), "Structural damage features extracted by convolutional neural networks from mode shapes", Appl. Sci., 10(12), 4247. https://doi.org/10.3390/app10124247 DOI |
8 | Huang, J.Y., Wu, Z.W., Shi, J., Gao, Y. and Wang, D.Z. (2018), "Influence of track irregularities in high-speed Maglev transportation systems", Smart Struct. Syst., Int. J., 21(5), 571-582. https://doi.org/10.12989/sss.2018.21.5.571 DOI |
9 | Ma, Z.R., Gao, L., Zhong, Y.L., Ma, S. and An, B.L. (2020), "Arching detection method of slab track in high-speed railway based on track geometry data", Appl. Sci., 10(19), 6799. https://doi.org/10.3390/app10196799 DOI |
10 | Goodall, R.M. (2008), "Generalised design models for EMS maglev", Proceedings of 20th International Conference on Magnetically Levitated Systems and Linear Drives, San Diego, CA, USA, December. |
11 | Li, H.T., Xu, H.Y., Tian, X.D., Wang, Y., Cai, H.Y., Cui, K.R. and Chen, X.D. (2020a), "Bridge crack detection based on SSENets", Appl. Sci., 10(12), 4230. https://doi.org/10.3390/app10124230 DOI |
12 | Kabo, E., Nielsen, J.C.O. and Ekberg, A. (2006), "Prediction of dynamic train-track interaction and subsequent material deterioration in the presence of insulated rail joints", Vehicle Syst. Dyn., 44, 718-729. https://doi.org/10.1080/00423110600885715 DOI |
13 | Kim, K.J, Han, J.B., Han, H.S. and Yang, S.J. (2015), "Coupled vibration analysis of Maglev vehicle-guideway while standing still or moving at low speeds", Vehicle Syst. Dyn., 53(4) 587-601. http://dx.doi.org/10.1080/00423114.2015.1013039 DOI |
14 | Kohavi, R. (1995), "A study of cross-validation and bootstrap for accuracy estimation and model selection", Proceedings of 14th International Joint Conference on Artificial Intelligence (IJCAI '95), San Francisco, CA, USA, August. |
15 | Li, X.Q., Zhai, M.D., Li, X.L. and Dong, W.H. (2020b), "Research on suppression strategy for track dislocation interference in medium-low speed maglev train", Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, June. |
16 | Deng, L., Chu, H.H., Shi, P., Wang, W. and Kong, X. (2020), "Region-based CNN method with deformable modules for visually classifying concrete cracks", Appl. Sci., 10(7), 2528. https://doi.org/10.3390/app10072528 DOI |
17 | Luo, L.X., Feng, M.Q., Wu, J.P. and Leung, R.Y. (2019), "Autonomous pothole detection using deep region-based convolutional neural network with cloud computing", Smart Struct. Syst., Int. J., 24(6), 745-757. https://doi.org/10.12989/sss.2019.24.6.745 DOI |
18 | Marino, F., Distante, A., Mazzeo, P.L. and Stella, E. (2007), "A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection", IEEE Transact. Syst. Man Cybernet. Part C (Applications and Reviews), 37(3), 418-428. https://doi.org/10.1109/TSMCC.2007.893278 DOI |
19 | Ohtsuka, T. and Iguchi, M. (1982), "Maglev dynamics and ride quality: past, present and future", Proceedings of 2nd International Seminar on the Superconductive Magnetic Levitated Train, Miyazaki, Japan, November. |
20 | Pham, H.C., Ta, Q.B., Kim, J.T., Ho, D.D., Tran, X.L. and Huynh, T.C. (2020a), "Bolt-loosening monitoring framework using an image-based deep learning and graphical model", Sensors, 20(12), 3382. https://doi.org/10.3390/s20123382 DOI |
21 | Faghih-Roohi, S., Hajizadeh, S., Nunez, A., Babuska, R. and De Schutter, B. (2016), "Deep convolutional neural networks for detection of rail surface defects", Proceedings of 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, July. |
22 | Gibert, X., Patel, V.M. and Chellappa, R. (2017), "Deep multitask learning for railway track inspection", IEEE Transact. Intell. Transport. Syst., 18(1), 153-164. https://doi.org/10.1109/TITS.2016.2568758 DOI |
23 | Ioffe, S. and Szegedy, C. (2015), "Batch normalization: accelerating deep network training by reducing internal covariate shift", Proceedings of 32nd International Conference on Machine Learning, Lile, France, July. |
24 | Wu, D., Sun, X., Chang, Y., Xu, W., Huang, J., Wu, Z. and Wang, D. (2019), "The temperature effect analysis of high-speed maglev transit", Proceedings of 4th International Conference on Automatic Control and Mechatronic Engineering (ACME 2019), Chongqing, China, May. |
25 | Sato, Y., Matsuura, A., Miura, S. and Satoh, Y. (1985), "Development of guideway for maglev", Proceedings of 7th International Conference on Maglev Transport'85, Tokyo, Japan, September. |
26 | Scherer, D., Muller, A. and Behnke, S. (2010), "Evaluation of pooling operations in convolutional architectures for object recognition", Proceedings of 20th International Conference on Artificial Neural Networks (ICANN), Thessaloniki, Greece, September. |
27 | Teng, Z.Q., Teng, S., Zhang, J.Q., Chen, G.F. and Cui, F.S. (2020), "Structural damage detection based on real-time vibration signal and convolutional neural network", Appl. Sci., 10(14), 4720. https://doi.org/10.3390/app10144720 DOI |
28 | Yau, J.D. (2009), "Response of a maglev vehicle moving on a series of guideways with differential settlement", J. Sound Vib., 324, 3-5. https://doi.org/10.1016.jsv.2009.02.031 DOI |
29 | Zhang, L. and Huang, J.Y. (2018), "Thermal effect on dynamic performance of high-speed maglev train/guideway system", Struct. Eng. Mech., Int. J., 68(4), 459-473. https://doi.org/10.12989/sem.2018.68.4.459 DOI |
30 | Zhang, D.K., Gao, S.B., Yu, L. and Zhan, D. (2017), "Dynamic detection method of medium-low speed maglev F-track seams based on machine vision", CES Transact. Electr. Mach. Syst., 1(4), 343-353. https://doi.org/10.23919/TEMS.2017.8241355 DOI |
31 | Alberts, T.E., Hanasoge, A.M. and Omran, A.M. (2011), "On the influence of structural flexibility on feedback control stability for magnetically suspended vehicles", J. Dyn. Syst. Measur. Control, 133(5), 051010. https://doi.org/10.1115/1.4003802 DOI |
32 | Zhao, X.F., Zhang, Y. and Wang, N.N. (2019), "Bolt loosening angle detection technology using deep learning", Struct. Control Health Monitor., 26(1), e2292. https://doi.org/10.1002/stc.2292 DOI |
33 | Zheng, R.H., Xiong, C., Deng, X.B., Li, Q.S. and Li, Y. (2020), "Assessment of earthquake destructive power to structures based on machine learning methods", Appl. Sci., 10(18), 6210. https://doi.org/10.3390/app10186210 DOI |
34 | Zhou, D.F., Yu, P.C., Wang, L.C. and Li, J. (2017), "An adaptive vibration control method to suppress the vibration of the maglev train caused by track irregularities", J. Sound Vib., 408, 331-350. https://doi.org/10.1016/j.jsv.2017.07.037 DOI |
35 | Li, X.Y., Liu, Y.Z., Zhou, W.W. and Wu, J. (2015), "Research on the influence on running vehicle from non-coplanar disturbance of four magnetic pole surfaces of middle-low speed maglev train tracks", Proceedings of 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, Nanchang, China, June. |
36 | Park, J.H., Kim, T.H. and Kim, J.T. (2015), "Image-based bolt-loosening detection technique of bolt Joint in steel bridges", Proceedings of 6th International Conference on Advances in Experimental Structural Engineering, Urbana-Champaign, IL, USA. |
37 | Duan, Y.F., Chen, Q.Y., Zhang, H.M., Yun, C.B., Wu, S.K. and Zhu, Q. (2019), "CNN-based damage identification method of tied-arch bridge using spatial-spectral information", Smart Struct. Syst., Int. J., 23(5), 507-520. https://doi.org/10.12989/sss.2019.23.5.507 DOI |
38 | Sung, H.K., Jho, J.M., Bae, D.K., Rho, K.S., Lee, J.M., Yoo, M.H. and Nam, Y.Y. (2006), "A fuzzy based treatment to reduce airgap disturbance at the rail joints with step-wise rail joint", Proceedings of 19th International Conference on Magnetically Levitated Systems and Linear Drives, Dresden, Germany, September. |
39 | Pham, M.T., Kim, J.M. and Kim, C.H. (2020b), "Accurate bearing fault diagnosis under variable shaft speed using convolutional neural networks and vibration spectrogram", Appl. Sci., 10(18), 6385. https://doi.org/10.3390/app10186385 DOI |
40 | Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M. and Inman, D.J. (2017), "Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks", J. Sound Vib., 388, 154-170. https://doi.org/10.1016/j.jsv.2016.10.043 DOI |
41 | Fujie, J. (1989), "Current status of EDS system in Japan", Proceedings of 11th International Conference on Magnetically Levitated Systems and Linear Drives, Yokohama, Japan, July. |
42 | Ye, X.W., Jin, T. and Yun, C.B. (2019), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Struct. Syst., Int. J., 24(5), 567-585. https://doi.org/10.12989/sss.2019.24.5.567 DOI |
43 | Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S. and Buyukozturk, O. (2017), "Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types", Comput.-Aided Civil Infrastr. Eng., 33(9), 731-747. https://doi.org/10.1111/mice.12334 DOI |
44 | Kim, J.J., Kim, A.R. and Lee, S.W. (2020), "Artificial neural network-based automated crack detection and analysis for the inspection of concrete structures", Appl. Sci., 10(22), 8105. https://doi.org/10.3390/app10228105 DOI |
45 | Li, Y.J., Yu, P.C., Zhou, D.F. and Li, J. (2018), "Magnetic flux feedback strategy to suppress the gap fluctuation of low-speed maglev train caused by track steps", Proceedings of 37th Chinese Control Conference (CCC), Wuhan, China, July. |
46 | Chen, J.W., Liu, Z.G., Wang, H.R., Nunez, A. and Han, Z.W. (2018), "Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network", IEEE Transact. Instrument. Measur., 67(2), 257-269. https://doi.org/10.1109/TIM.2017.2775345 DOI |
47 | Dangre, H. (2019), "A review on insulated rail joints (IRJ) failure analysis", Int. J. Adv. Res. Publicat., 3(1), 5-9. |
48 | Dung, C.V., Sekiya, H., Hirano, S., Okatani, T. and Miki, C. (2019), "A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks", Automat. Constr., 102, 217-229. https://doi.org/10.1016/j.autcon.2019.02.013 DOI |
49 | Feng, J., Li, F.M., Lu, S.X., Liu, J.H. and Ma, D.Z. (2017), "Injurious or noninjurious defect identification from MFL images in pipeline inspection using convolutional neural network", IEEE Transact. Instrument. Measur., 66(7), 1883-1892. https://doi.org/10.1109/TIM.2017.2673024 DOI |
![]() |