Citation: | Yongkui SUN, Yuan CAO, Peng LI, et al., “Fault Diagnosis for Railway Point Machines Using VMD Multi-scale Permutation Entropy and ReliefF Based on Vibration Signals,” Chinese Journal of Electronics, vol. 34, no. 1, pp. 1–8, 2025 doi: 10.23919/cje.2023.00.258 |
[1] |
J. T. Yin, A. D’Ariano, Y. H. Wang, et al., “Timetable coordination in a rail transit network with time-dependent passenger demand,” European Journal of Operational Research, vol. 295, no. 1, pp. 183–202, 2021. doi: 10.1016/j.ejor.2021.02.059
|
[2] |
Y. Cheng, J. T. Yin, and L. X. Yang, “Robust energy-efficient train speed profile optimization in a scenario-based position-time-speed network,” Frontiers of Engineering Management, vol. 8, no. 4, pp. 595–614, 2021. doi: 10.1007/s42524-021-0173-1
|
[3] |
D. Q. Huang, S. P. Li, N. Qin, et al., “Fault diagnosis of high-speed train bogie based on the improved-CEEMDAN and 1-D CNN algorithms,” IEEE Transactions on Instrumentation and Measurement, vol. 70, article no. 3508811, 2021. doi: 10.1109/TIM.2020.3047922
|
[4] |
T. Wen, G. Xie, Y. Cao, et al., “A DNN-based channel model for network planning in train control systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2392–2399, 2022. doi: 10.1109/TITS.2021.3093025
|
[5] |
Y. Cao, Z. X. Zhang, F. L. Cheng, et al., “Trajectory optimization for high-speed trains via a mixed integer linear programming approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 17666–17676, 2022. doi: 10.1109/TITS.2022.3155628
|
[6] |
S. Su, J. F. She, K. C. Li, et al., “A nonlinear safety equilibrium spacing-based model predictive control for virtually coupled train set over gradient terrains,” IEEE Transactions on Transportation Electrification, vol. 8, no. 2, pp. 2810–2824, 2022. doi: 10.1109/TTE.2021.3134669
|
[7] |
H. Z. Dong, Z. B. Tian, J. W. Spencer, et al., “Coordinated control strategy of railway multisource traction system with energy storage and renewable energy,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 12, pp. 15702–15713, 2023. doi: 10.1109/TITS.2023.3271464
|
[8] |
J. T. Yin, X. L. Ren, R. H. Liu, et al., “Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach,” Reliability Engineering & System Safety, vol. 219, article no. 108183, 2022. doi: 10.1016/j.ress.2021.108183
|
[9] |
Y. K. Sun, Y. Cao, H. T. Liu, et al., “Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals,” Transportation Safety and Environment, vol. 5, no. 2, article no. tdac048, 2023. doi: 10.1093/tse/tdac048
|
[10] |
H. T. Chen and B. Jiang, “A review of fault detection and diagnosis for the traction system in high-speed trains,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 2, pp. 450–465, 2020. doi: 10.1109/TITS.2019.2897583
|
[11] |
F. Wang, S. H. Sun, Y. Cao, et al., “Optimal design of tractive layout for minimizing the insufficient displacement of railway turnout,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 11, pp. 12597–12613, 2023. doi: 10.1109/TITS.2023.3289210
|
[12] |
X. Li, X. Zhong, H. D. Shao, et al., “Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-riemannian kernel ridge regression,” Reliability Engineering & System Safety, vol. 216, article no. 108018, 2021. doi: 10.1016/j.ress.2021.108018
|
[13] |
D. Q. Huang, Y. Z. Fu, N. Qin, et al., “Fault diagnosis of high-speed train bogie based on LSTM neural network,” Science China Information Sciences, vol. 64, no. 1, article no. 119203, 2021. doi: 10.1007/s11432-018-9543-8
|
[14] |
L. L. Chen, N. Qin, X. Dai, et al., “Fault diagnosis of high-speed train bogie based on capsule network,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6203–6211, 2020. doi: 10.1109/TIM.2020.2968161
|
[15] |
H. T. Chen, B. Jiang, S. X. Ding, et al., “Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 1700–1716, 2022. doi: 10.1109/TITS.2020.3029946
|
[16] |
C. Bian, S. K. Yang, T. T. Huang, et al., “Degradation state mining and identification for railway point machines,” Reliability Engineering & System Safety, vol. 188 pp. 432–443, 2019. doi: 10.1016/j.ress.2019.03.044
|
[17] |
M. Fidali, P. Wojciechowski, and A. Pełka, “Fault detection of railway point machine using diagnostic models,” in Proceedings of the 6th International Congress on Technical Diagnostics, Gliwice, Poland, pp. 275–285, 2016.
|
[18] |
J. Y. Kim, H. Y. Kim, D. Park, et al., “Modelling of fault in RPM using the GLARMA and INGARCH model,” Electronics Letters, vol. 54, no. 5, pp. 297–299, 2018. doi: 10.1049/el.2017.3398
|
[19] |
V. Atamuradov, F. Camci, S. Baskan, et al., “Failure diagnostics for railway point machines using expert systems,” in Proceedings of 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Cargese, France, pp. 1–5, 2009.
|
[20] |
S. Z. Huang, Z. X. Wu, F. Zhang, et al., “Recognition of signal fault curves based on dynamic time warping for rail transportation,” in Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019, Y. Qin, L. M. Jia, B. M. Liu, et al., Eds. Springer, Singapore, pp. 185–195, 2019.
|
[21] |
N. Wang, H. G. Wang, L. M. Jia, et al., “Turnout health assessment based on dynamic time warping,” in Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019, Y. Qin, L. M. Jia, B. M. Liu, et al., Eds. Springer, Singapore, pp. 517–527, 2019.
|
[22] |
H. Kim, J. Sa, Y. Chung, et al., “Fault diagnosis of railway point machines using dynamic time warping,” Electronics Letters, vol. 52, no. 10, pp. 818–819, 2016. doi: 10.1049/el.2016.0206
|
[23] |
S. Z. Huang, F. Zhang, R. J. Yu, et al., “Turnout fault diagnosis through dynamic time warping and signal normalization,” Journal of Advanced Transportation, vol. 2017, article no. 3192967, 2017. doi: 10.1155/2017/3192967
|
[24] |
D. X. Ou, R. Xue, and K. Cui, “A data-driven fault diagnosis method for railway turnouts,” Transportation Research Record:Journal of the Transportation Research Board, vol. 2673, no. 4, pp. 448–457, 2019. doi: 10.1177/0361198119837222
|
[25] |
K. H. Narges, M. Ahmad, and G. M. Fereydoun, “A hybrid fault diagnosis scheme for railway point machines by motor current signal analysis,” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 236, no. 9, pp. 1026–1034, 2022. doi: 10.1177/09544097211061918
|
[26] |
Z. Shi, Z. C. Liu, and J. Lee, “An auto-associative residual based approach for railway point system fault detection and diagnosis,” Measurement, vol. 119 pp. 246–258, 2018. doi: 10.1016/j.measurement.2018.01.062
|
[27] |
M. Vileiniskis, R. Remenyte-Prescott, and D. Rama, “A fault detection method for railway point systems,” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 230, no. 3, pp. 852–865, 2016. doi: 10.1177/0954409714567487
|
[28] |
Z. Li, Z. Yin, T. Tang, et al., “Fault diagnosis of railway point machines using the locally connected autoencoder,” Applied Sciences, vol. 9, no. 23, article no. 5139, 2019. doi: 10.3390/app9235139
|
[29] |
J. Lee, H. Choi, D. Park, et al., “Fault detection and diagnosis of railway point machines by sound analysis,” Sensors, vol. 16, no. 4, article no. 549, 2016. doi: 10.3390/s16040549
|
[30] |
Y. K. Sun, Y. Cao, P. Li, et al., “Vibration-based fault diagnosis for railway point machines using VMD and multiscale fluctuation-based dispersion entropy,” Chinese Journal of Electronics, in press.
|
[31] |
R. F. R. Junior, I. A. dos Santos Areias, M. M. Campos, et al., “Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals,” Measurement, vol. 190, article no. 110759, 2022. doi: 10.1016/j.measurement.2022.110759
|
[32] |
Y. Wang, M. M. Yang, Y. Li, et al., “A multi-input and multi-task convolutional neural network for fault diagnosis based on bearing vibration signal,” IEEE Sensors Journal, vol. 21, no. 9, pp. 10946–10956, 2021. doi: 10.1109/JSEN.2021.3061595
|
[33] |
Y. K. Sun, Y. Cao, P. Li, et al., “Sound based degradation status recognition for railway point machines based on soft-threshold wavelet Denoising, WPD, and ReliefF,” IEEE Transactions on Instrumentation and Measurement, vol. 73, article no. 3507609, 2024. doi: 10.1109/TIM.2023.3334370
|
[34] |
R. Abdelkader, A. Kaddour, and Z. Derouiche, “Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method,” The International Journal of Advanced Manufacturing Technology, vol. 97, no. 5, pp. 3099–3117, 2018. doi: 10.1007/s00170-018-2167-7
|
[35] |
K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, 2014. doi: 10.1109/TSP.2013.2288675
|
[36] |
S. Mohanty, K. K. Gupta, and K. S. Raju, “Comparative study between VMD and EMD in bearing fault diagnosis,” in Proceedings of the 2014 9th International Conference on Industrial and Information Systems, Gwalior, India, pp. 1–6, 2014.
|
[37] |
M. A. Ambusaidi, X. J. He, P. Nanda, et al., “Building an intrusion detection system using a filter-based feature selection algorithm,” IEEE Transactions on Computers, vol. 65, no. 10, pp. 2986–2998, 2016. doi: 10.1109/TC.2016.2519914
|
[38] |
H. Rouhani, A. Fathabadi, and J. Baartman, “A wrapper feature selection approach for efficient modelling of gully erosion susceptibility mapping,” Progress in Physical Geography:Earth and Environment, vol. 45, no. 4, pp. 580–599, 2021. doi: 10.1177/0309133320979897
|
[39] |
I. I. M. Manhrawy, M. Qaraad, and P. El-Kafrawy, “Hybrid feature selection model based on relief-based algorithms and regulizer algorithms for cancer classification,” Concurrency and Computation:Practice and Experience, vol. 33, no. 17, article no. e6200, 2021. doi: 10.1002/cpe.6200
|
[40] |
Y. Cao, Y. K. Sun, P. Li, et al., “Vibration-based fault diagnosis for railway point machines using multi-domain features, ensemble feature selection and SVM,” IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 176–184, 2024. doi: 10.1109/TVT.2023.3305603
|
[41] |
C. Bandt and B. Pompe, “Permutation entropy: A natural complexity measure for time series,” Physical Review Letters, vol. 88, no. 17, article no. 174102, 2002. doi: 10.1103/PhysRevLett.88.174102
|
[42] |
Y. K. Sun, Y. Cao, P. Li, et al., “Entropy feature fusion-based diagnosis for railway point machines using vibration signals based on kernel principal component analysis and support vector machine,” IEEE Intelligent Transportation Systems Magazine, vol. 15, no. 6, pp. 96–108, 2023. doi: 10.1109/MITS.2023.3295376
|
[43] |
H. Li, T. Liu, X. Wu, et al., “An optimized VMD method and its applications in bearing fault diagnosis,” Measurement, vol. 166, article no. 108185, 2020. doi: 10.1016/j.measurement.2020.108185
|
[44] |
R. Q. Yan, Y. B. Liu, and R. X. Gao, “Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines,” Mechanical Systems and Signal Processing, vol. 29 pp. 474–484, 2012. doi: 10.1016/j.ymssp.2011.11.022
|