Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Railway train inspection robot based on intelligent recognition technology

  • Original article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

In order to improve the operation effect of the railway train inspection robot, this paper applies image recognition technology to the robot system. The hardware part of the robot system includes: data acquisition station, data processing transfer station and inspection analysis center. First of all, this paper improves the traditional image recognition algorithm and builds an image recognition system suitable for railway train inspection requirements. Secondly, this paper combines the operating requirements of the railway train inspection robot to collect multiple sets of data through the vision system to establish a database, and collects trust data from the railway department to construct a standard database. In addition, this paper builds the intelligent identification system of this paper through simulation, obtains the railway train inspection robot, verifies the recognition performance of the railway train inspection robot through multiple sets of data, and counts the recognition accuracy rate. Finally, this paper verifies the reliability of the intelligent robot system constructed in this paper through experimental research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Banić M, Miltenović A, Pavlović M et al (2019) Intelligent machine vision based railway infrastructure inspection and monitoring using UAV[J]. Facta Univ Series Mech Eng 17(3):357–364

    Article  Google Scholar 

  • Bin Osman MH, Kaewunruen S, Jack A (2018) Optimisation of schedules for the inspection of railway tracks[J]. Proc Inst Mech Eng Part F J Rail Rapid Transit 232(6):1577–1587

    Article  Google Scholar 

  • Falamarzi A, Moridpour S, Nazem M (2019) A review on existing sensors and devices for inspecting railway infrastructure[J]. J Kejuruteraan 31(1):1–10

    Article  Google Scholar 

  • Han SH, Cho MS, Yu YK et al (2017) A Study on cantilever deformation inspection method using image processing[J]. Trans Korean Inst Elect Eng 66(6):988–994

    Google Scholar 

  • Krishna BR, Seshendra D, Raja GG et al (2017) Railway track fault detection system by using ir sensors and bluetooth technology[J]. Asian J Appl Sci Technol (AJAST) 1(6):82–84

    Google Scholar 

  • Li Y, Liu Z, Liu X et al (2019) High-speed electromagnetic train wheel inspection using a Kalman-model-based demodulation algorithm[J]. IEEE Sens J 19(16):6833–6843

    Article  Google Scholar 

  • Li P, Long Z, Yang Z (2020) RF energy harvesting for batteryless andz maintenance-free condition monitoring of railway tracks[J]. IEEE Internet Things J 8(5):3512–3523

    Article  Google Scholar 

  • Liu S, Wang Q, Luo Y (2019a) A review of applications of visual inspection technology based on image processing in the railway industry[J]. Transp Safe Environ 1(3):185–204

    Article  Google Scholar 

  • Liu J, Huang Y, Zou Q et al (2019b) Learning visual similarity for inspecting defective railway fasteners[J]. IEEE Sens J 19(16):6844–6857

    Article  Google Scholar 

  • Nan G, Gao Y (2018) Automated visual inspection of multipattern train components using gradient information and feature fusion under the illumination-variant condition[J]. Proc Inst Mech Eng Part F J Rail Rapid Transit 232(5):1500–1513

    Article  Google Scholar 

  • Rajamäki J, Vippola M, Nurmikolu A et al (2018) Limitations of eddy current inspection in railway rail evaluation[J]. Proc Inst Mech Eng Part F J Rail Rapid Transit 232(1):121–129

    Article  Google Scholar 

  • Shen Y, Liu Z, Zhang G (2018) PAC interaction inspection using real-time contact point tracking[J]. IEEE Trans Instrum Meas 68(10):4051–4064

    Article  Google Scholar 

  • Tomiyama T, Sato T, Okada K et al (2018) Railway rollingrolling-stockock-assignmentassignment-scheduling algorithm for minimizing inspection cost[J]. IAENG Int J Appl Math 48(2):1–10

    MathSciNet  Google Scholar 

  • Torabi M, Mousavi SGM, Younesian D (2018) A high accuracy imaging and measurement system for wheel diameter inspection of railroad vehicles[J]. IEEE Trans Industr Electron 65(10):8239–8249

    Article  Google Scholar 

  • Torabi M, Mousavi GSM, Younesian D (2021) A new flexible laser beam profiler for the inspection of train wheels[J]. Proc Inst Mech Eng Part F J Rail Rapid Transit 235(2):215–225

    Article  Google Scholar 

  • Wang SM, Liao CL, Ni YQ (2020) A machine vision system based on driving recorder for automatic inspection of rail curvature[J]. IEEE Sens J 21(10):11291–11300

    Article  Google Scholar 

  • Xiao L, Wu B, Hu Y et al (2019) A hierarchical features-based model for freight train defect inspection[J]. IEEE Sens J 20(5):2671–2678

    Article  Google Scholar 

  • Ye J, Stewart E, Roberts C (2019) Use of a 3D model to improve the performance of laser-based railway track inspection[J]. Proc Inst Mech Eng Part F J Rail Rapid Transit 233(3):337–355

    Article  Google Scholar 

  • Zhang Z, Hou T (2018) Edge extraction of train wheel tread damage based on improved canny algorithm[J]. Railway Standard Design 62(1):148–150

    Google Scholar 

  • Zhong J, Liu Z, Han Z et al (2018) A CNN-based defect inspection method for catenary split pins in high-speed railway[J]. IEEE Trans Instrum Meas 68(8):2849–2860

    Article  Google Scholar 

  • Zhou F, Song Y, Liu L et al (2018) Automated visual inspection of target parts for train safety based on deep learning[J]. IET Intel Trans Syst 12(6):550–555

    Article  Google Scholar 

Download references

Funding

The research is supported by: 1. Project of production, education and research of Zhengzhou Railway Vocational and Technical College «Design and research of multifunctional trolley for trough detection» (Project No.: 2020CXY001); 2. Project of production, education and research of Zhengzhou Railway Vocational and Technical College «Design and Research on Ore Powder Removal Device of Port Railway Crossing Flange Groove» (Project No.: 2020CXY002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Meng Lv.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, M., Niu, C. Railway train inspection robot based on intelligent recognition technology. Int J Syst Assur Eng Manag 14, 648–656 (2023). https://doi.org/10.1007/s13198-021-01446-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-021-01446-8

Keywords

Navigation