Skip to main content
SpringerLink
Log in

Failure diagnosis of electro-hydraulic servo valve based on SA-PSO-SVM

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The electro-hydraulic servo valve (EHSV) is a crucial element of the electro-hydraulic servo control system connecting electrical and hydraulic parts, and its performance can influence the work efficiency of the entire control system. To identify the types of faults in EHSVs accurately and solve the problems in traditional diagnostic algorithms, a fault diagnostic model using a simulated annealing particle swarm optimized (SA-PSO) support vector machine (SVM) is constructed in the paper. An SA-PSO optimized SVM parametric fault diagnosis model is built, taking static no-load flow, pressure, and internal leakage as the sample input data, and a prescribed label is viewed as the result output. Compared with the models constructed by SVM and PSO-SVM model, the advantages of the model built are proven. Classification accuracy can reach 99.3 %, and high stability can be achieved.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. Kang, Z. Yuan and M. Tariq Sadiq, Numerical simulation and experimental research on flow force and pressure stability in a nozzle-flapper servo valve, Processes, 8(11) (2020) 1404.

    Article  Google Scholar 

  2. H. Heineken, S. Ulrich and R. Bruns, A high response electrorheological servo valve for active vibration decoupling, Actuator 2018: 16th International Conference on New Actuators (2018) 1–4.

  3. S. H. Wang, T. W. Ni and Z. G. Yang, Failure analysis on abnormal blockage of electro-hydraulic servo valve in digital electric hydraulic control system of 125 MW thermal power plant, Eng. Fai. Anal., 123 (2021) 105294.

    Article  Google Scholar 

  4. F. X. Zheng, S. Li, C. Ding, J. T. Zhao and J. Ruan, Theoretical and experimental research on the cartridge two-dimensional (2D) electro-hydraulic servo valve, Adv. Mech. Eng., 13 (2) (2021).

  5. L. Lu, S. Long and K. Zhu, A numerical research on vortex street flow oscillation in the double flapper nozzle servo valve, Processes, 7(10) (2019) 721.

    Article  Google Scholar 

  6. H. Heineken, S. Ulrich, R. Bruns and S. Schneider, High-response electrorheological servo valve, J. Intell. Mater. Syst. Struct., 31(2) (2020) 297–307.

    Article  Google Scholar 

  7. L. Ma, H. Yan, C. Cai and Y. K. Ren, Mechanism of temperature-induced zero drift on electro-hydraulic servo valve, AIP Adv., 11(6) (2021) 065210.

    Article  Google Scholar 

  8. L. Quan, Y. Miao, C. Guo and Q. Zhang, A novel composite control algorithm for restraining nonlinear superfluous forces, IEEE Access, 7 (2019) 51841–51855.

    Article  Google Scholar 

  9. C. Wang, L. Quan, Z. Jiao and S. Zhang, Nonlinear adaptive control of hydraulic system with observing and compensating mismatching uncertainties, IEEE Trans. Control Syst. Technol., 26(3) (2018) 927–938.

    Article  Google Scholar 

  10. B. K. Saha, Y. Wu and S. Li, Deflector optimization in reducing cavitation intensity in the pilot stage of deflector jet servo-valve, 2019 IEEE 8th International Conference on Fluid Power and Mechatronics (FPM) (2019) 1500–1507.

  11. S. Shahkar, Y. Shen and K. Khorasani, Diagnosis, prognosis and health monitoring of electro hydraulic servo valves (EHSV) using particle filters, 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) (2019) 385–390.

  12. X. Wang, S. Lin, S. Wang, J. Shi and C. Zhang, A multi-fault diagnosis strategy of electro-hydraulic servo actuation system based on extended Kalman filter, 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (2017) 614–619.

  13. J. Wang, J. Miao, J. Wang, F. Yang, K. L. Tsui and Q. Miao, Fault diagnosis of electrohydraulic actuator based on multiple source signals: an experimental investigation, Neurocomputing, 417 (2020) 224–238.

    Article  Google Scholar 

  14. H. Y. Pan, S. N. Liu, Y. M. Yao and Y. L. Jiang, Fault diagnostic of electro-hydraulic servo valve, Adv. Mat. Res., 619 (2013) 259–263.

    Google Scholar 

  15. J. Chai and X. Jin, Research on fault diagnosis of servo valve based on deep learning, 2017 Prognostics and System Health Management Conference (PHM) (2017) 1–7.

  16. G. Chen, D. Yang, W. Gao, L. Chen, Z. Hua and C. Ai, Research on fault method of electro-hydraulic servo valve based on deep neural network, 2019 IEEE 8th International Conference on Fluid Power and Mechatronics (FPM) (2019) 393–397.

  17. L. Quan, H. X. Guo, S. Sheng and L. Li, Fault diagnosis of electro hydraulic servo valves based on GA+ LM algorithm optimized BP neural networks, China Mechanical Engineering, 29(5) (2018) 505–510.

    Google Scholar 

  18. C. Jia, K. Kang, W. Gao, D. Yang, L. Chen and C. Ai, Fault prediction of electro-hydraulic servo valve based on CNN+ LSTM neural network, Chinese Hydraulics and Pneumatics, 12 (2020) 173–181.

    Google Scholar 

  19. C. Liu, Y. Wang, T. Pan and G. Zheng, Fault diagnosis of electro-hydraulic servo valve using extreme learning machine, Int. Trans. Electr. Energ. Syst., 30(7) (2020) e12419.

    Article  Google Scholar 

  20. H. Huang, Y. Wang and H. Zong, Support vector machine classification over encrypted data, Applied Intelligence (2021) 1–11.

  21. S. Rezvani, X. Wang and F. Pourpanah, Intuitionistic fuzzy twin support vector machines, IEEE Trans. Fuzzy. Syst., 27(11) (2019) 2140–2151.

    Article  Google Scholar 

  22. M. Kafai and K. Eshghi, CROification: accurate kernel classification with the efficiency of sparse linear SVM, IEEE Trans. Pattern Anal. Mach. Intell., 41(1) (2019) 34–48.

    Article  Google Scholar 

  23. D. Freitas, L. G. Lopes and F. Morgado-Dias, Particle swarm optimisation: a historical review up to the current developments, Entropy, 22(3) (2020) 362.

    Article  MathSciNet  Google Scholar 

  24. H. Deng and C. Xie, An improved particle swarm optimization algorithm for inverse kinematics solution of multi-DOF serial robotic manipulators, Soft Comput., 25(21) (2021) 13695–13708.

    Article  Google Scholar 

  25. T. Hayashida, I. Nishizaki, S. Sekizaki and Y. Takamori, Improvement of two-swarm cooperative particle swarm optimization using immune algorithms and swarm clustering, 2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA) (2019) 101–107.

  26. X. Liu, G. G. Wang and L. Wang, LSFQPSO: quantum particle swarm optimization with optimal guided Lévy flight and straight flight for solving optimization problems, Eng. Comput., (2021) 1–32.

  27. C. Ji, Q. Chen and C. Song, Improved particle swarm optimization geomagnetic matching algorithm based on simulated annealing, IEEE Access, 8 (2020) 226064–226073.

    Article  Google Scholar 

  28. H. Tang, R. Chen, Y. Li, Z. Peng, S. Guo and Y. Du, Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: an application from a casting workshop, Appl. Soft Comput., 78 (2019) 176–194.

    Article  Google Scholar 

  29. J. Zhang, P. C. Wen and Y. Shen, Adaptive noise canceller design based on chaotic simulated annealing particle swarm optimization algorithm, 2021 15th International Symposium on Medical Information and Communication Technology (ISMICT) (2021) 122–126.

Download references

Author information

Authors and Affiliations

  1. Engineering Institute of Advanced Manufacturing and Modern Equipment Technology, Jiangsu University, Jiangsu, 212013, China

    Yongzhong Fu & Lianjie Cai

  2. Zhenjiang Silian Mechatronic Technology Co., Zhenjiang, Jiangsu, 212000, China

    Gang Zheng

Authors
  1. Yongzhong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lianjie Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianjie Cai.

Additional information

Yongzhong Fu is currently an Associate Professor of the Department of Mechanical Engineering of Jiangsu University. He is currently the Deputy Director of the Department of Mechanical and Electrical Engineering. He is mainly engaged in CNC technology research work, deep learning in the field of vision research work and fault diagnosis research field.

Lianjie Cai is studying for his Bachelor’s and Master’s degrees in Mechanical Engineering from Jiangsu University, China. His research interests include machine learning, deep learning, and fault diagnosis.

Gang Zheng is an Engineer at Zhenjiang Silian Mechatronics Technology Co. He is currently mainly engaged in hydraulic transmission and control, industrial robots, electric-hydraulic proportional direction valve, and other research work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, Y., Cai, L. & Zheng, G. Failure diagnosis of electro-hydraulic servo valve based on SA-PSO-SVM. J Mech Sci Technol 36, 5971–5976 (2022). https://doi.org/10.1007/s12206-022-1113-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-022-1113-7

Keywords

Search

Navigation