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International Conference on Advanced Hybrid Information Processing

ADHIP 2019: Advanced Hybrid Information Processing pp 140–150Cite as

Research on Fatigue Life Prediction Method of Ballastless Track Based on Big Data

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Abstract

In order to improve the precision of fatigue life prediction of ballastless track, a method for predicting fatigue life of ballastless track based on big data is proposed. The big data model is constructed to analyze the fatigue life cycle of ballastless track. Big data mining and feature extraction are used to extract the fatigue life cycle of ballastless track. Combining with the particle swarm optimization method, the feature classification of the failure state trend of ballastless track construction is carried out, and the information fusion is carried out according to the characteristic parameters of the failure state of ballastless track construction. The expert system model for predicting fatigue life of ballastless track construction is established and the fatigue life of ballastless track is predicted by association rule mining method. The simulation results show that the precision of fatigue life prediction of ballastless track is high, and the strength and life cycle of ballastless track are analyzed.

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References

  1. Hu, S., Ding, Z., Ni, Q.: Beamforming optimisation in energy harvesting cooperative full-duplex networks with self-energy recycling protocol. IET Commun. 10(7), 848–853 (2016)

    Article  Google Scholar 

  2. Seo, D.W., Lee, J.H., Lee, H.S.: Optimal coupling to achieve maximum output power in a WPT system. IEEE Trans. Power Electron. 31(6), 3994–3998 (2016)

    Article  Google Scholar 

  3. Dai, H., Huang, Y., Li, C., et al.: Energy-efficient resource allocation for device-to-device communication with WPT. IET Commun. 11(3), 326–334 (2017)

    Article  Google Scholar 

  4. Helmy, A., Hedayat, A., Al-Dhahir, N.: Robust weighted sum-rate maximization for the multi-stream MIMO interference channel with sparse equalization. IEEE Trans. Commun. 60(10), 3645–3659 (2015)

    Article  Google Scholar 

  5. Alfaro, V.M., Vilanovab, R.: Robust tuning of 2DoF five-parameter PID controllers for inverse response controlled processes. J. Process Control 23(4), 453–462 (2013)

    Article  Google Scholar 

  6. Han, D., Chen, X., Lei, Y., et al.: Real-time data analysis system based on Spark Streaming and its application. J. Comput. Appl. 37(5), 1263–1269 (2017)

    Google Scholar 

  7. Sun, D.W., Zhang, G.Y., Zheng, W.M.: Big data stream computing, technologies and instances. J. Softw. 25(4), 839–862 (2014)

    Google Scholar 

  8. Hao, S.G., Zhang, L., Muhammad, G.: A union authentication protocol of cross-domain based on bilinear pairing. J. Softw. 8(5), 1094–1100 (2013)

    Article  Google Scholar 

  9. Zikui, M.A., Chen, W.: Friction torque calculation method of ball bearings based on rolling creepage theory. J. Mech. Eng. 53(22), 219–224 (2017)

    Article  Google Scholar 

  10. Zhou, S.B., Xu, W.X.: A novel clustering algorithm based on relative density and decision graph. Control Decis. 33(11), 1921–1930 (2018)

    MATH  Google Scholar 

  11. He, H., Tan, Y.: Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering. Appl. Soft Comput. 55, 238–252 (2017)

    Article  Google Scholar 

  12. Zhu, Y., Zhu, X., Wang, J.: Time series motif discovery algorithm based on subsequence full join and maximum clique. J. Comput. Appl. 39(2), 414–420 (2019)

    Google Scholar 

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Authors and Affiliations

  1. Railway Engineering College, Wuhan Railway Vocational College of Technology, Wuhan, 430205, China

    Ailin Wang

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  1. Ailin Wang
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Correspondence to Ailin Wang .

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Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Guan Gui

  2. Harbin Engineering University, Harbin, China

    Lin Yun

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, A. (2019). Research on Fatigue Life Prediction Method of Ballastless Track Based on Big Data. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_15

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  • DOI: https://doi.org/10.1007/978-3-030-36405-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36404-5

  • Online ISBN: 978-3-030-36405-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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