Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault
Abstract
The rapid development of railway transportation towards high speed, high density and heavy load has led to even higher requirements for the safety of railway signal equipment. The safety of railway signal equipment is an important part of ensuring railway traffic safety, thus, it is very necessary to study a system that can diagnose the fault of railway signal equipment according to the actual situation. This article utilizes the deep learning algorithm of artificial intelligence for investigating the interlocking faults in the railway transportation. This paper uses ADASYN data synthesis method to synthesize few category samples, uses TF-IDF to extract features and transform vectors, and proposes a deep learning integration method based on combined weight. The results show that BiGRU has better overall classification performance when evaluated on the index of primary and secondary fault classification accuracy. The classification accuracy improvement of 5% is achieved for primary fault classification and the comprehensive evaluation index of secondary fault classification is improved by about 9%. It was revealed that when compared with ADASYN + BiLSTM neural network, the comprehensive evaluation index of primary fault classification accuracy is improved by about 6%, and the comprehensive evaluation index of secondary fault classification is improved by about 10%. It is demonstrated that deep learning integration is an effective method to improve the classification performance of turnout fault diagnosis model.
Full Text:
PDFReferences
Kong, J. (2020, December). Application and research of artificial intelligence in digital library. In International conference on Big Data Analytics for Cyber-Physical-Systems (pp. 318-325). Springer, Singapore.
https://doi.org/10.1007/978-981-33-4572-0_47
Paek, S., & Kim, N. (2021). Analysis of worldwide research trends on the impact of artificial intelligence in education. Sustainability, 13(14), 7941.
https://doi.org/10.3390/su13147941
Dobias, R., & Kubatova, H. (2004, August). FPGA based design of the railway's interlocking equipments. In Euromicro Symposium on Digital System Design, 2004. DSD 2004. (pp. 467-473). IEEE.
1109/DSD.2004.1333312
Skiribou, C., Elbahhar, F., & Elassali, R. (2021). DMRS-based channel estimation for railway communications in tunnel environments. Vehicular Communications, 29, 100340.
https://doi.org/10.1016/j.vehcom.2021.100340
Kiedrowski, P., & Saganowski, Ł. (2021). Method of Assessing the Efficiency of Electrical Power Circuit Separation with the Power Line Communication for Railway Signs Monitoring. Transport and Telecommunication, 22(4), 407-416.
2478/ttj-2021-0031
Yang, J., Bai, X., Zhang, Z., Yang, M., Pan, P., Liu, T., & Tao, T. (2021, May). Research on the application of BDS/GIS/RS technology in the high speed railway infrastructure maintenance. In IOP Conference Series: Earth and Environmental Science (Vol. 783, No. 1, p. 012168). IOP Publishing.
1088/1755-1315/783/1/012168
Lin, J., Hu, X., Dang, J., & Wu, Z. (2019). Traffic model of machine-type communication for railway signal equipment based on MMPP. IET Microwaves, Antennas & Propagation, 13(8), 1072-1079.
https://doi.org/10.1049/iet-map.2018.6004
Wang, X., Guo, J., Jiang, L., Fu, J., & Li, B. (2016, August). Intelligent fault diagnosis and prediction technologies for condition based maintenance of track circuit. In 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT) (pp. 276-283). IEEE.
1109/ICIRT.2016.7588745
Sahal, R., Breslin, J. G., & Ali, M. I. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of manufacturing systems, 54, 138-151.
https://doi.org/10.1016/j.jmsy.2019.11.004
Cao, Y., Li, P., & Zhang, Y. (2018). Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing. Future Generation Computer Systems, 88, 279-283.
https://doi.org/10.1016/j.future.2018.05.038
Dong, C. Z., Ye, X. W., & Jin, T. (2018). Identification of structural dynamic characteristics based on machine vision technology. Measurement, 126, 405-416.
https://doi.org/10.1016/j.measurement.2017.09.043
Jia, Z., & Sharma, A. (2021). Review on engine vibration fault analysis based on data mining. Journal of Vibroengineering, 23(6), 1433-1445.
https://doi.org/10.21595/jve.2021.21928
Yin, M., Li, K., & Cheng, X. (2020). A review on artificial intelligence in high-speed rail. Transportation Safety and Environment, 2(4), 247-259.
https://doi.org/10.1093/tse/tdaa022
Ren, X., Li, C., Ma, X., Chen, F., Wang, H., Sharma, A., & Masud, M. (2021). Design of multi-information fusion based intelligent electrical fire detection system for green buildings. Sustainability, 13(6), 3405.
https://doi.org/10.3390/su13063405
Sharma, D., Kaur, R., Sandhir, M., & Sharma, H. (2021). Finite element method for stress and strain analysis of FGM hollow cylinder under effect of temperature profiles and inhomogeneity parameter. Nonlinear Engineering, 10(1), 477-487.
https://doi.org/10.1515/nleng-2021-0039
Afandizadeh, S., & Rad, H. B. (2021). Developing a model to determine the number of vehicles lane changing on freeways by Brownian motion method. Nonlinear Engineering, 10(1), 450-460.
https://doi.org/10.1515/nleng-2021-0036
Shabaz, M., Sharma, A., Al Ajrawi, S., & Estrela, V. V. (2022). Multimedia-based emerging technologies and data analytics for Neuroscience as a Service (NaaS). Neuroscience Informatics, 2(3), 100067.
https://doi.org/10.1016/j.neuri.2022.100067
Meher, M., & Rostamy, D. (2021). Hybrid of differential quadrature and sub-gradients methods for solving the system of Eikonal equations. Nonlinear Engineering, 10(1), 436-449.
https://doi.org/10.1515/nleng-2021-0035
Mi, Z., Wang, T., Sun, Z., & Kumar, R. (2021). Vibration signal diagnosis and analysis of rotating machine by utilizing cloud computing. Nonlinear Engineering, 10(1), 404-413.
https://doi.org/10.1515/nleng-2021-0032
Wang, H., Sharma, A., & Shabaz, M. (2022). Research on digital media animation control technology based on recurrent neural network using speech technology. International Journal of System Assurance Engineering and Management, 13(1), 564-575.
https://doi.org/10.1007/s13198-021-01540-x
Yousaf, B., Qaisrani, M. A., Khan, M. I., Sahar, M. S. U., & Tahir, W. (2021). Numerical and experimental analysis of the cavitation and study of flow characteristics in ball valve. Nonlinear Engineering, 10(1), 535-545.
https://doi.org/10.1515/nleng-2021-0044
Singh, P. K., & Sharma, A. (2022). An intelligent WSN-UAV-based IoT framework for precision agriculture application. Computers and Electrical Engineering, 100, 107912.
https://doi.org/10.1016/j.compeleceng.2022.107912
Zeng, H., Dhiman, G., Sharma, A., Sharma, A., & Tselykh, A. (2021). An IoT and Blockchain‐based approach for the smart water management system in agriculture. Expert Systems, e12892.
https://doi.org/10.1111/exsy.12892
Sharma, A., & Singh, P. K. (2021). UAV‐based framework for effective data analysis of forest fire detection using 5G networks: An effective approach towards smart cities solutions. International Journal of Communication Systems, e4826.
https://doi.org/10.1002/dac.4826
Sharma, A., Singh, P. K., & Kumar, Y. (2020). An integrated fire detection system using IoT and image processing technique for smart cities. Sustainable Cities and Society, 61, 102332.
https://doi.org/10.1016/j.scs.2020.102332
Zang, Y., Shangguan, W., Cai, B., Wang, H., & Pecht, M. G. (2019). Methods for fault diagnosis of high-speed railways: A review. Proceedings of the institution of mechanical engineers, part O: journal of risk and reliability, 233(5), 908-922.
https://doi.org/10.1177/1748006X18823932
Ting, L., Khan, M., Sharma, A., & Ansari, M. D. (2022). A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing. Journal of Intelligent Systems, 31(1), 221-236.
https://doi.org/10.1515/jisys-2022-0012
Minea, M., Dumitrescu, C. M., & Dima, M. (2021). Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion. Sensors, 21(20), 6876.
https://doi.org/10.3390/s21206876
Luo, J., Wu, M., Gopukumar, D., & Zhao, Y. (2016). Big data application in biomedical research and health care: a literature review. Biomedical informatics insights, 8, BII-S31559.
https://doi.org/10.4137/BII.S31559
Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining for the internet of things: literature review and challenges. International Journal of Distributed Sensor Networks, 11(8), 431047.
https://doi.org/10.1155/2015/431047
DOI: https://doi.org/10.31449/inf.v46i3.3961
This work is licensed under a Creative Commons Attribution 3.0 License.