Skip to main content
Log in

Fault Diagnosis Method of Escalator Step System Based on Vibration Signal Analysis

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript


For the problem that escalator fault diagnosis is difficult to realize, this paper proposes a fault diagnosis method based on vibration signal analysis. The vibration signal is collected from three parts: step guide rail, main drive shaft and main engine. The wavelet threshold denoising algorithm based on Ensemble Empirical Mode Decomposition (EEMD) is used to denoise the vibration signal. The signal characteristics are extracted, and the fault detection is performed through the Support Vector Machine (SVM) fault detection model. For the fault signal, the improved envelope spectrum analysis method is used to extract the characteristic frequency and corresponding amplitude to form the characteristic vector, and the Support Vector Machine for Particle Swarm Optimization (PSO-SVM) algorithm is used to identify the location of the fault. The experimental results show that this method has high accuracy in the fault diagnosis of escalator step system.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others


  1. Q. Meng, “Research on fault diagnosis method for main drive shaft bearing of escalator based on EEMD-SVM,” Machinery & Electronics, vol. 38, no. 5, pp. 51–53+58, 2020.

    Google Scholar 

  2. F. Elasha, C. Ruiz-Cárcel, D. Mba, G. Kiat, I. Nze, and G. Yebra, “Pitting detection in worm gearboxes with vibration analysis,” Engineering Failure Analysis, vol. 38, pp. 231–241, 2014.

    Google Scholar 

  3. V. Sharma and A. Parey, “Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed,” Engineering Failure Analysis, vol. 107, p. 104204, 2020.

    Article  Google Scholar 

  4. J. C. Hsiao, S. Kumar, and T. Y. Kam, “Fault diagnosis method for worm gearbox using convolutional network and ensemble learning,” Journal of Physics: Conference Series, vol. 1509, no. 1, 2020.

  5. W. J. Wang and P. D. McFadden, “Application of wavelets to gearbox vibration signal for fault detection,” Journal of Sound and Vibration, vol. 192, no. 5, pp. 927–939, 1996.

    Article  Google Scholar 

  6. X. Gong, L. Ding, W. Du, and H. Wang, “Gear fault diagnosis using dual channel data fusion and EEMD method,” Procedia Engineering, vol. 174, pp. 918–926, 2017.

    Article  Google Scholar 

  7. X. Chen and Z. Feng, “Induction motor stator current analysis for planetary gearbox fault diagnosis under time-varying speed conditions,” Mechanical Systems and Signal Processing, vol. 140, p. 106691, 2020.

    Article  Google Scholar 

  8. Q. Yang, M. Huang, and W. Yan, “Particle swarm optimization-based empirical mode decomposition—kernel independent component analysis joint approach for diagnosing wind turbine gearbox with multiple faults,” Transactions of the Institute of Measurement and Control, vol. 40, no. 6, pp. 1836–1845, 2020.

    Article  Google Scholar 

  9. X. Ma, X. Shi, and J. Zhang, “Modeling and experimental investigation on the vibration of main drive chain in escalator,” Proc. of INTER-NOISE and NOISE-CON Congress and Conference, vol. 259, no. 7, pp. 2069–2080, 2019.

    Google Scholar 

  10. X. Liu, F. Ren, and Q. Wang, “Fatigue life analysis of escalator main drive chain,” Proc. of International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), pp. 482–431, 2020.

  11. V. A. Pliem, S. Scheibelhofer, and G. Brasseur, “Crack detection on an escalator handrail,” Proc. of the 19th IEEE Instrumentation and Measurement Technology Conference, vol. 2, pp. 1001–1005, 2002.

    Google Scholar 

  12. F. Ren, C. Chen, X. Liang, and B. Li, “Friction and wear behavior of escalator step auxiliary wheels,” Proc. of IOP Conference Series: Materials Science and Engineering, vol. 452, no. 2, 2018.

  13. H. Cherif, A. Benakcha, I. Laib, S. E. Chehaidia, A. Menacer, and A. G. Olabi, “Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor,” Energy, vol. 212, p. 118684, 2020.

    Article  Google Scholar 

  14. M. Skowron, T. Orlowska-Kowalska, and C. T. Kowalski, “Convolutional neural network-based stator current data-driven incipient stator fault diagnosis of inverter-fed induction motor,” Energies, vol. 13, no. 6, 2020.

  15. D. Zhang, G. Feng, Y. Shi, and D. Srinivasan, “Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no.2, pp. 319–333, 2021.

    Article  MathSciNet  Google Scholar 

  16. X. Li, J. Wang, and B. Zhang, “Fault diagnosis of rolling element bearing weak fault based on sparse decomposition and broad learning network,” Transactions of the Institute of Measurement and Control, vol. 42, no. 2, pp. 428–431, 2020.

    Article  Google Scholar 

  17. P. S. Kumar, L. A. Kumaraswamidhas, and S. K. Laha, “Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis,” Transactions of the Institute of Measurement and Control, vol. 41, no. 7, pp. 1923–1932, 2019.

    Article  Google Scholar 

  18. H. Li, T. Liu, X. Wu, and Q. Chen, “Application of optimized variational mode decomposition based on kurtosis and resonance frequency in bearing fault feature extraction,” Transactions of the Institute of Measurement and Control, vol. 42, no. 3, pp. 518–527, 2020.

    Article  Google Scholar 

  19. D. Zhang, Y. Chen, F. Guo, H. R. Karimi, H. Dong, and Q. Xuan, “A new interpretable learning method for fault diagnosis of rolling bearings,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–10, 2021.

    Article  Google Scholar 

  20. L. Xin, M. Li, H. Wang, and H. Yan, “Research and application of data acquisition for on-line state monitoring of escalator in urban rail transit station,” Construction & Design for Engineering, vol. 2017, no. 2, pp. 176–178, 2017.

    Google Scholar 

  21. D. He, M. Rao, and B. Nong, “Design of on-line monitoring and early warning system for subway escalators,” China Elevator, vol. 30, no. 21, pp. 12–14, 2019.

    Google Scholar 

  22. F. Chao and W. Jie, “Application of Internet of Things technology in airport escalator management,” Internet of Things Technologies, vol. 8, no. 3, pp. 113–115, 2018.

    Google Scholar 

  23. H. Ao, “Application of frequency analysis in escalator debugging,” Mechanical and Electrical Information, vol. 9, pp. 74–76, 2014.

    Google Scholar 

  24. D. Peng, “Application of Frequency analysis method in the adjustment of the vibration of the escalator,” Science and Technology & Innovation, vol. 19, pp. 108–109, 2015.

    Google Scholar 

  25. H. Tsutada, T. Hirai, Y. Itoh, and S. Shiga, “Fault diagnosis of escalator using inspection step equipped with acceleration and sound sensors,” Transactions of the Society of Instrument and Control Engineers, vol. 43, no. 9, pp. 735–740, 2007.

    Article  Google Scholar 

  26. L. Tang and Y. Yao, “Application of EVA vibration analysis system to escalators,” China Elevator, 2017.

  27. L. Gao and J. Chen, “Vibration test of escalator cascade and analysis of vibration causes,” China Elevator, 2016.

  28. X. Sua and H. Li, “Denoising of shock signal based on EMD and wavelet threshold,” Computer Measurement & Control, vol. 25, no. 1, pp. 204–208+220, 2017.

    Google Scholar 

  29. H. Wang, Y. Liu, and Y. Liao, “Fault diagnosis method for rolling bearings based on fast spectral kurtosis and orthogonal matching pursuit algorithm,” Journal of Vibration and Shock, vol. 39, no. 19, pp. 78–83+100, 2020.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Fuqiang You.

Additional information

Publisher’s Note

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

This work was supported by the National Key Research and Development Program of China (No.2019YFF0302203) and the Fundamental Research Funds for the Central Universities (N2104008).

Fuqiang You received his Ph.D. degree from the School of Electronic Information and Electronic Engineering of Shanghai Jiaotong University, China in 2005. Currently, he is an associate professor in Northeastern University, China. His research interests include fault diagnosis and fault-tolerant control of industrial control system.

Dianlong Wang received his bachelor’s degree in electrical engineering and automation from the School of Information of Ludong University in 2019. He is studying for a master’s degree in control science and engineering from Northeastern University. His research interests include fault diagnosis and fault-tolerant control.

Guanghai Li received his doctorate from South China University of Technology. He engaged his postdoctoral research at Tsinghua University in 2003. In June 2005, he entered China Special Equipment Testing Research Institute. He mainly engaged in special equipment safety detection and evaluation of scientific research and engineering application work, research direction for special equipment inspection and evaluation.

Chunhua Chen received his bachelor’s and professional master’s degrees in automation instrument from the Department of Automatic Control of Northeast University.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

You, F., Wang, D., Li, G. et al. Fault Diagnosis Method of Escalator Step System Based on Vibration Signal Analysis. Int. J. Control Autom. Syst. 20, 3222–3232 (2022).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: