Skip to main content
SpringerLink
Log in

PSO with improved local unimodal search ability for incipient pump fault identification

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Incipient fault identification of high performance hydraulic pump is crucial for aviation hydraulic systems. Accurate and timely identification of these faults become difficult because of nonlinearities, noise, and uncertainties. An analysis of the particle swarm optimization (PSO) potential to solve this problem with the help of system model and online measurements is carried out in this article. The PSO algorithm with novel set of parameters has been proposed to improve the fault identification performance. Aim is to deal with the challenge of explosion of swarms as the search space is kept as small as possible in case of estimation of incipient fault. It can be fulfilled by ensuring that the swarms converge with smaller steps. With the aid of an existing convergence criterion, this work offers a parameter selection guideline and investigates whether the modified parameters guarantee convergence and stability for improved local region search. As the acceleration coefficients seem a promising parameter to achieve this objective so it is given more focus in this analysis. To give a measure to the convergence, swarms diversity over a run is calculated, demonstrated and compared for different set of PSO parameters. Fault severity and operating pressure are also taken into account to analyze the parameter estimation performance. Finally, some benchmark problems have been solved to show the validity of the suggested approach on more challenging problems.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15

Similar content being viewed by others

References

  1. Wang Z L, Chen B and Qiu L H 2000 Trends of future aircraft hydraulic system. Hydraul. Pneum. Seals 1: 14–18

    Google Scholar 

  2. Liu Y, Zuo M J, Li Y F and Huang H Z 2015 Dynamic reliability assessment for multi-state systems utilizing system-level inspection data. IEEE Trans. Reliab. 64(4): 1287–1299

    Article  Google Scholar 

  3. Gao Y, Zhang Q and Kong X 2003 Wavelet-based pressure analysis for hydraulic pump health diagnosis. Trans. ASAE 46(4): 969

    Google Scholar 

  4. Wang J and Hu H 2006 Vibration-based fault diagnosis of pump using fuzzy technique. Measurement 39(2): 176–185

    Article  Google Scholar 

  5. Jiang W L and Wu S 2010 Multi-data fusion fault diagnosis method based on SVM and evidence theory. Chin. J. Sci. Instrum. 31(8): 1738–1743

    Google Scholar 

  6. Lu C, Ma N and Wang Z 2011 Fault detection for hydraulic pump based on chaotic parallel RBF network. EURASIP J. Adv. Sign. Process. 1: 1–10

    Google Scholar 

  7. Li S, Zhang P, Li B and Wang G 2014 Fault feature selection method for axial piston pump based on quantum genetic algorithm. China Mech. Eng. 25(12): 1659–1664

    Google Scholar 

  8. Lu C, Wang S and Zhang C 2016 Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 230(16): 2913–2928

    Article  Google Scholar 

  9. Lan Y, Hu J, Huang J, Niu L, Zeng X, Xiong X and Wu B 2018 Fault diagnosis on slipper abrasion of axial piston pump based on extreme learning machine. Measurement 124: 378–385

    Article  Google Scholar 

  10. Zheng Z, Wang Z, Zhu Y, Tang S and Wang B 2019 Feature extraction method for hydraulic pump fault signal based on improved empirical wavelet transform. Processes 7(11): 824

    Article  Google Scholar 

  11. Zhu Y, Li G, Wang R, Tang S, Su H and Cao K 2021 Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization. Appl. Acoust. 183: 108336

    Article  Google Scholar 

  12. Xiao C, Tang H, Ren Y and Kumar A 2022 Fuzzy entropy assisted singular spectrum decomposition to detect bearing faults in axial piston pump. Alex. Eng. J. 61(8): 5869–5885

    Article  Google Scholar 

  13. Piao X 2011 Research about pressure selection of hydraulic system in civil aircraft. Fluid Power Transmission Control 6: 22–24

    Google Scholar 

  14. Juan C and Jing L 2015 Typical failure modes and accelerated lifetime test methods for aircraft hydraulic pump. Chin. Hydraul. Pneum. 7: 1

    Google Scholar 

  15. Ruixiang Z Tingqi L Jianding H and Dongchao Y 2002 Fault diagnosis of airplane hydraulic pump. In: Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No. 02EX527), Vol. 4, pp. 3150–3152

  16. Willsky A S 1976 A survey of design methods for failure detection in dynamic systems. Automatica 12(6): 601–611

    Article  MathSciNet  MATH  Google Scholar 

  17. Isermann R and Freyermuth B 1991 Process fault diagnosis based on process model knowledge: part I-principles for fault diagnosis with parameter estimation. J. Dyn. Sys. Meas. Control. 113(4): 620–626

    Article  MATH  Google Scholar 

  18. Isermann R 1993 Fault diagnosis of machines via parameter estimation and knowledge processing–tutorial paper. Automatica 29(4): 815–835

    Article  MathSciNet  MATH  Google Scholar 

  19. Liu Y, Lin P, Li Y F and Huang H Z 2014 Bayesian reliability and performance assessment for multi-state systems. IEEE Trans. Reliab. 64(1): 394–409

    Article  Google Scholar 

  20. Palazzolo J J Scheunemann L D and Hartin J R 2008 Leakage fault detection method for axial-piston variable displacement pumps. In: IEEE Aerospace Conference, pp. 1–8

  21. Songyong L, Xiaohui L, Xiaoming H and Xinxia C 2014 Fault diagnosis of pump valve spring based on improved singularity analysis. J. Vibroeng. 16(2): 704–712

    Google Scholar 

  22. Liu Y, Shi Y, Zhou Q and Xiu R 2016 A sequential sampling strategy to improve the global fidelity of metamodels in multi-level system design. Struct. Multidiscip. Optim. 53(6): 1295–1313

    Article  MathSciNet  Google Scholar 

  23. Samantaray A K, Ghoshal S K, Chakraborty S and Mukherjee A 2005 Improvements to single-fault isolation using estimated parameters. Simulation 81(12): 827–845

    Article  Google Scholar 

  24. Mishra S K, Tripathi J P, Das J and Ghoshal S K 2018 Application of parallel multi-model simulation method for condition monitoring of a power hydraulic system. Arab. J. Sci. Eng. 43(9): 4501–4515

    Article  Google Scholar 

  25. Zhonghai M, Shaoping W, Jian S, Tongyang L and Xingjian W 2018 Fault diagnosis of an intelligent hydraulic pump based on a nonlinear unknown input observer. Chin. J. Aeronaut. 31(2): 385–394

    Article  Google Scholar 

  26. Piltan F, Prosvirin A E, Jeong I, Im K and Kim J M 2019 Rolling-element bearing fault diagnosis using advanced machine learning-based observer. Appl. Sci. 9(24): 5404

    Article  Google Scholar 

  27. Zhuo S, Gaillard A, Xu L, Liu C, Paire D and Gao F 2020 An observer-based switch open-circuit fault diagnosis of DC–DC converter for fuel cell application. IEEE Trans. Ind. Appl. 56(3): 3159–3167

    Article  Google Scholar 

  28. Zhang Z, Zhang P and Leifeld T 2022 Reduced-order observer design for fault diagnosis of Boolean control networks. Automatica 146: 110618

    Article  MathSciNet  MATH  Google Scholar 

  29. Yu M, Xiao C, Jiang W, Yang S and Wang H 2018 Fault diagnosis for electromechanical system via extended Analytical Redundancy Relations. IEEE Trans. Ind. Inform. 14(12): 5233–5244

    Article  Google Scholar 

  30. Chi C Deng P Zhang J Pan Z Li T and Wu Z 2019 May A fault diagnosis method of temperature sensor based on analytical redundancy. In: Prognostics and System Health Management Conference (PHM-Paris) IEEE, pp. 156–162

  31. Mohammadi A and Ramezani A 2020 An active actuator fault-tolerant control of a quadrotor based on Analytical Redundancy Relations. Iran. J. Sci. Technol. Trans. Electr. Eng. 44(3): 1069–1079

    Article  Google Scholar 

  32. Ghoshal S K, Samantaray A K and Samanta S 2009 Model based fault diagnosis, fault tolerant control and reconfiguration of hydraulic and thermo-fluid processes using analytical redundancy. Int. J. Autom. Control 3(4): 363–384

    Article  Google Scholar 

  33. Mohammadi A and Ramezani A 2023 A robust model predictive control-based method for fault detection and fault tolerant control of quadrotor UAV. Trans. Inst. Meas. Control 45(1): 37–48

    Article  Google Scholar 

  34. Eberhart R C and Shi Y 2000 July Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation CEC00 (Cat. No. 00TH8512), IEEE, Vol. 1, pp. 84–88

  35. Clerc M and Kennedy J 2002 The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evolut. Comput. 6(1): 58–73

    Article  Google Scholar 

  36. Harrison K R, Engelbrecht A P and Ombuki-Berman B M 2018 Self-adaptive particle swarm optimization: a review and analysis of convergence. Swarm Intell. 12(3): 187–226

    Article  Google Scholar 

  37. Sun L, Song X and Chen T 2019 An improved convergence particle swarm optimization algorithm with random sampling of control parameters. J. Control Sci. Eng.. https://doi.org/10.1155/2019/7478498

    Article  MATH  Google Scholar 

  38. Xu G, Luo K, Jing G, Yu X, Ruan X and Song J 2020 On convergence analysis of multi-objective particle swarm optimization algorithm. Eur. J. Oper. Res. 286(1): 32–38

    Article  MathSciNet  MATH  Google Scholar 

  39. Tong X T, Choi K P, Lai T L and Wong W K 2021 Stability bounds and almost sure convergence of improved particle swarm optimization methods. Res. Math. Sci. 8(2): 1–16

    Article  MathSciNet  MATH  Google Scholar 

  40. Huang H, Qiu J and Riedl K 2022 On the global convergence of particle swarm optimization methods. arXiv preprint arXiv:2201.12460

  41. Choi Y P, Ju H and Koo D 2023 Convergence analysis of particle swarm optimization in one dimension. Appl. Math. Lett. 137: 108481

    Article  MathSciNet  MATH  Google Scholar 

  42. Lin C C 2013 Dynamic router node placement in wireless mesh networks: a PSO approach with constriction coefficient and its convergence analysis. Inf. Sci. 232: 294–308

    Article  MathSciNet  MATH  Google Scholar 

  43. Dasgupta K and Banerjee S 2014 February Short-term hydrothermal scheduling using particle swarm optimization with constriction factor and inertia weight approach. In: First International Conference on Automation, Control, Energy and Systems (ACES) IEEE, pp 1–6

  44. Patwardhan R P and Mhetre S L 2015 October Effect of constriction factor on minimization of transmission power loss using particle Swarm optimization. In: International Conference on Energy Systems and Applications, pp. 152–157

  45. Puri V, Chauhan Y K and Singh N 2016 Economic load dispatch problem using particle swarm optimization with inertial weight and constriction factor. Thammasat. Int. J. Sci. Technol. 21(2): 52–60

    Google Scholar 

  46. Sakamoto S Ozera K Ikeda M and Barolli L 2017 August Performance evaluation of WMNs by WMN-PSOSA simulation system considering constriction and linearly decreasing inertia weight methods. In: International Conference on Network-Based Information Systems, pp. 3–13

  47. Tripathi J P and Ghoshal S 2017 Combining inertia and constriction technique in the PSO applied to fault identification in a hydraulic system. Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 231(14): 2730–2740

    Article  Google Scholar 

  48. Merugumalla M K and Navuri P K 2019 Chaotic inertia weight and constriction factor-based PSO algorithm for BLDC motor drive control. Int. J. Process Syst. Eng. 5(1): 30–52

    Article  Google Scholar 

  49. Balu K and Mukherjee V 2020 Siting and sizing of distributed generation and shunt capacitor banks in radial distribution system using constriction factor particle swarm optimization. Electr. Power Compon. Syst. 48(6–7): 697–710

    Article  Google Scholar 

  50. Pervez I, Sarwar A, Pervez A, Tariq M and Zaid M 2021 An improved maximum power point tracking (MPPT) of a partially shaded solar PV system using PSO with constriction factor (PSO-CF). In: Advances in Electromechanical Technologies, pp. 499–507

  51. Eberhart R and Kennedy J 1995 October A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43

  52. Shi Y and Eberhart R C 1999 July Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Vol. 3, pp. 1945–1950

  53. Shi Y and Eberhart R C 1998 March Parameter selection in particle swarm optimization. In: International Conference on Evolutionary Programming, pp. 591–600

  54. Harrison K R, Engelbrecht A P and Ombuki-Berman B M 2016 Inertia weight control strategies for particle swarm optimization. Swarm Intell. 10(4): 267–305

    Article  Google Scholar 

  55. Harrison K R, Engelbrecht A P and Ombuki-Berman B M 2018 Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm. Swarm Evolut. Comput. 41: 20–35

    Article  Google Scholar 

  56. Poli R 2009 Mean and variance of the sampling distribution of particle swarm optimizers during stagnation. IEEE Trans. Evolut. Comput. 13(4): 712–721

    Article  Google Scholar 

  57. Liu Q 2015 Order-2 stability analysis of particle swarm optimization. Evolut. Comput. 23(2): 187–216

    Article  MathSciNet  Google Scholar 

  58. Tripathi J P, Ghoshal S K, Dasgupta K and Das J 2017 Bond graph modelling of a hydraulic cylinder-actuated planar manipulator. J. Braz. Soc. Mech. Sci. Eng. 39(11): 4275–4287

    Article  Google Scholar 

  59. Athanasatos P and Costopoulos T 2012 Proactive fault finding in a 4/3-way direction control valve of a high pressure hydraulic system using the bond graph method with digital simulation. Mech. Mach. Theory 50: 64–89

    Article  Google Scholar 

  60. Product catalogue of the fixed displacement pump EIC-B-1001-0. Japan: Yuken Kogyo Co. Ltd., 2014

  61. Borutzky W 2015 Isolation of multiple parametric faults from a hybrid model. In: Bond Graph Model-Based Fault Diagnosis of Hybrid Systems, pp 123–148

  62. Olorunda O and Engelbrecht A P 2008, June Measuring exploration/exploitation in particle swarms using swarm diversity. In: IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1128–1134

  63. Lynn N and Suganthan P N 2015 Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evolut. Comput. 24: 11–24

    Article  Google Scholar 

  64. Li D, Guo W, Lerch A, Li Y, Wang L and Wu Q 2021 An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization. Swarm Evolut. Comput. 60: 100789

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Uttam Kumar Singh, Jay P Tripathi & Kishore Khanna

Authors
  1. Uttam Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jay P Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kishore Khanna
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jay P Tripathi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, U.K., Tripathi, J.P. & Khanna, K. PSO with improved local unimodal search ability for incipient pump fault identification. Sādhanā 48, 172 (2023). https://doi.org/10.1007/s12046-023-02197-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-023-02197-x

Keywords

Search

Navigation