Skip to main content
SpringerLink
Log in

An improved social mimic optimization algorithm and its application in bearing fault diagnosis

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

As a key component of rotating machinery, it is of great significance for the timely diagnosis of bearing weak faults. Stochastic resonance is widely used for its special signal enhancement pattern, and the combination of system parameters determines its actual output effect. Because the social mimic optimization algorithm has the advantages of few parameters, fast convergence speed and strong exploitation capability, it is used to optimize the parameters of stochastic resonance system in this paper. Aiming at the problem that it is easy to fall into local optimum during optimization, inspired by the learning habits of elite, an elite social mimic optimization (ESMO) algorithm is proposed. Its improvement mainly includes five parts: integration of learning efficiency, exchange learning, looking for successors, innovation and breakthrough, elimination mechanism. And its superiority is verified by the comparison of 29 standard benchmark functions and 10 other well-known optimization algorithms. Aiming at the discontinuity of optimization space, the concept of survival rate (surr) is proposed, and the effectiveness of the ESMO algorithm in optimizing discontinuous variables is analyzed and verified. Aiming at the disadvantage that stochastic resonance can only process small frequency signals, a bearing weak fault diagnosis method based on frequency exchange and parameter compensation stochastic resonance is proposed. To verify its application ability in other fault diagnosis methods, a classification and recognition method based on BP neural network is proposed. Finally, the effectiveness and superiority of the ESMO algorithm are verified by simulation signals and bearing experimental data. The above analysis results prove that the ESMO algorithm has certain scientific and engineering application value in the optimization field and engineering application.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41
Fig. 42

Similar content being viewed by others

Data availability

The authors declare that all data supporting the findings of this study are included within the article.

References

  1. Qiao Z, Lei Y, Li N (2019) Applications of stochastic resonance to machinery fault detection: a review and tutorial. Mech Syst Signal Process 122(MAY1):502–536. https://doi.org/10.1016/j.ymssp.2018.12.032

    Article  ADS  Google Scholar 

  2. Lu S, He Q, Wang J (2019) A review of stochastic resonance in rotating machine fault detection. Mech Syst Signal Process 116:230–260. https://doi.org/10.1016/j.ymssp.2018.06.032

    Article  ADS  Google Scholar 

  3. He L, Zhou X, Gang Z, Zhang T (2018) Stochastic resonance in time- delayed exponential monostable system driven by weak periodic signals. Phys Lett A 382:2431–2438. https://doi.org/10.1016/j.physleta.2018.06.002

    Article  ADS  MathSciNet  CAS  Google Scholar 

  4. Lu L, Wang F, Liu Y (2019) Levy noise-driven stochastic resonance in a coupled monostable system. Euro Phys J B Condens Matter Complex Syst 92(1):1–9. https://doi.org/10.1140/epjb/e2018-90520-y

    Article  MathSciNet  CAS  Google Scholar 

  5. Yang C, Yang J, Zhou D, Shuai Z, Litak G (2021) Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application. Philos Trans Royal Soc Math Phys Eng Sci 379(2192):1–18. https://doi.org/10.1098/rsta.2020.0239

    Article  MathSciNet  Google Scholar 

  6. Li J, Chen X, He Z (2013) Multi-stable stochastic resonance and its application research on mechanical fault diagnosis. J Sound Vib 332(22):5999–6015. https://doi.org/10.1016/j.jsv.2013.06.017

    Article  ADS  Google Scholar 

  7. Zhang G, Xu H, Zhang T (2020) Research and application of stochastic resonance mechanism of two-dimensional tetra-stable potential system. Chin J Sci Instrum 41(4):229–238. https://doi.org/10.19650/j.cnki.cjsi.J2006123

    Article  Google Scholar 

  8. He L, Jiang C, Zhang G, Zhang T (2020) Research on fault detection of the piecewise linear asymmetric system. Chin J Sci Instrum 41(2):226–234

    Google Scholar 

  9. López C, Zhong W, Lu S, Cong F, Cortese I (2017) Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection. J Sound Vib 411:34–46. https://doi.org/10.1016/j.jsv.2017.08.043

    Article  ADS  Google Scholar 

  10. Lopez C, Naranjo A, Lu S, Moore KJ (2022) Hidden markov model based stochastic resonance and its application to bearing fault diagnosis. J Sound Vibration. https://doi.org/10.1016/j.jsv.2022.116890

    Article  Google Scholar 

  11. Jiao S, Qiao X, Lei S, Jiang W (2019) A novel parameter-induced adaptive stochastic resonance system based on composite multi-stable potential model. Chin J Phys Taipei. 59:138–152. https://doi.org/10.1016/j.cjph.2019.02.031

    Article  MathSciNet  Google Scholar 

  12. He B, Huang Y, Wang D, Yan B, Dong D (2019) A parameter-adaptive stochastic resonance based on whale optimization algorithm for weak signal detection for rotating machinery. Measurement 136:658–667. https://doi.org/10.1016/j.measurement.2019.01.017

    Article  ADS  Google Scholar 

  13. Xin Z, Qiang M, Zhiwen L et al (2017) An adaptive stochastic resonance method based on grey wolf optimizer algorithm and its application to machinery fault diagnosis. ISA trans. https://doi.org/10.1016/j.isatra.2017.08.009

    Article  Google Scholar 

  14. Gao K, Xu X, Li J, Jiao S, Shi N (2021) Weak fault feature extraction for polycrystalline diamond compact bit based on ensemble empirical mode decomposition and adaptive stochastic resonance. Measurement. https://doi.org/10.1016/j.measurement.2021.109304

    Article  Google Scholar 

  15. Zheng Y, Ming H, Yi L, Li W (2020) Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm. Neural Comput Appl 32(1):1–12. https://doi.org/10.1007/s00521-018-3910-6

    Article  Google Scholar 

  16. Li M, Shi P, Zhang W, Han D (2020) Study on the optimal stochastic resonance of different bistable potential models based on output saturation characteristic and application. Chaos, Solitons Fractals 139:110098. https://doi.org/10.1016/j.chaos.2020.110098

    Article  MathSciNet  Google Scholar 

  17. Huang D, Yang J, Zhou D, Litak G (2020) Novel adaptive search method for bearing fault frequency using stochastic resonance quantified by amplitude-domain index. IEEE Trans Instrumen Measurement 69(1):109–121. https://doi.org/10.1109/TIM.2019.2890933

    Article  ADS  Google Scholar 

  18. Chahar V, Katoch S, Chauhan SS (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80(4):8091–8126. https://doi.org/10.1007/s11042-020-10139-6

    Article  Google Scholar 

  19. Kennedy J (2010) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 760–766. https://doi.org/10.1007/978-0-387-30164-8_630

    Chapter  Google Scholar 

  20. Fister I, Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13(1):34–46. https://doi.org/10.1016/j.swevo.2013.06.001

    Article  Google Scholar 

  21. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471. https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  Google Scholar 

  22. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132. https://doi.org/10.1016/j.amc.2009.03.090

    Article  MathSciNet  Google Scholar 

  23. Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl Based Syst 75:1–18. https://doi.org/10.1016/j.knosys.2014.07.025

    Article  Google Scholar 

  24. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  25. Sm A, Smm B, Al A (2014) Grey wolf optimizer. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  26. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  27. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  28. Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization: artificial ants as a computational intelligence technique. IEEE Comput Intell Magazine 1(4):28–39

    Article  Google Scholar 

  29. Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.103300

    Article  Google Scholar 

  30. Balochian S, Baloochian H (2019) Social mimic optimization algorithm and engineering applications. Expert Syst Appl 134:178–191. https://doi.org/10.1016/j.eswa.2019.05.035

    Article  Google Scholar 

  31. Al-Betar MA, Alyasseri Z, Awadallah MA, Doush IA (2020) Coronavirus herd immunity optimizer (CHIO). Neur Comput Appl. https://doi.org/10.21203/rs.3.rs-27214/v1

    Article  Google Scholar 

  32. Panigrahy D, Samal P (2021) Modified lightning search algorithm for optimization. Eng Appl Artif Intell 105:104419. https://doi.org/10.1016/j.engappai.2021.104419

    Article  Google Scholar 

  33. Houssein EH, Helmy ED, Rezk H, Nassef AM (2021) An enhanced archimedes optimization algorithm based on local escaping operator and orthogonal learning for PEM fuel cell parameter identification. Eng Appl Artif Intell 103:104309. https://doi.org/10.1016/j.engappai.2021.104309

    Article  Google Scholar 

  34. Kaveh A, Sheikholeslami R, Talatahari S, Keshvari-Ilkhichi M (2014) Chaotic swarming of particles: A new method for size optimization of truss structures. Adv Eng Softw 67:136–147. https://doi.org/10.1016/j.advengsoft.2013.09.006

    Article  Google Scholar 

  35. Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Design Eng 5(4):458–472. https://doi.org/10.1016/j.jcde.2017.02.005

    Article  Google Scholar 

  36. Luo J, Chen H, Qian Z, Xu Y, Hui H, Zhao X (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64:654–668. https://doi.org/10.1016/j.apm.2018.07.044

    Article  MathSciNet  Google Scholar 

  37. Ewees A, Elaziz MA, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172. https://doi.org/10.1016/j.eswa.2018.06.023

    Article  Google Scholar 

  38. Che Y, He D (2022) An enhanced seagull optimization algorithm for solving engineering optimization problems. Appl Intell 52(11):13043–13081. https://doi.org/10.1007/s10489-021-03155-y

    Article  Google Scholar 

  39. Elaziz MA, Abualigah L, Ewees AA, Al-qaness MAA, Mostafa RR, Yousri D et al (2022) Triangular mutation-based manta-ray foraging optimization and orthogonal learning for global optimization and engineering problems. Appl Inteli 53(7):7788–7817. https://doi.org/10.1007/s10489-022-03899-1

    Article  Google Scholar 

  40. Houssein EH, Hassan MH, Kamel S, Hussain K, Hashim FA (2022) Modified Lévy flight distribution algorithm for global optimization and parameters estimation of modified three- diode photovoltaic model. Appl Intell. https://doi.org/10.1007/s10489-022-03977-4

    Article  Google Scholar 

  41. Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98. https://doi.org/10.1016/j.cnsns.2012.06.009

    Article  ADS  MathSciNet  Google Scholar 

  42. Gaganpreet K, Sankalap A (2018) Chaotic whale optimization algorithm. J Comput Design Eng 3:275–284. https://doi.org/10.1016/j.jcde.2017.12.006

    Article  Google Scholar 

  43. Hua F, Hao L (2022) Improved sparrow search algorithm with multi-strategy integration and its application. Control Decis 37(1):10. https://doi.org/10.13195/j.kzyjc.2021.0582

    Article  MathSciNet  Google Scholar 

  44. Abed-alguni BH, Paul D, Hammad R (2022) Improved Salp swarm algorithm for solving single-objective continuous optimization problems. Appl Intell 52(15):17217–17236. https://doi.org/10.1007/s10489-022-03269-x

    Article  Google Scholar 

  45. Wang Y, Haowen Y, Dan L, Enhao L, Xinfa W, Yan W (2023) Optimization of BP for bearing fault diagnosis based on improved antlion algorithm. Comp Integrat Manufact Syst 1:1–21

    Google Scholar 

  46. Ghosh KK, Singh PK, Hong J, Zong WG, Sarkar R (2020) Binary social mimic optimization algorithm with x-shaped transfer function for feature selection. IEEE Access 8:87890–87906

    Article  Google Scholar 

  47. Thirumoorthy K, Britto JJJ (2022) A clustering approach for software defect prediction using hybrid social mimic optimization algorithm. Computing 104(12):2605–2633. https://doi.org/10.1007/s00607-022-01100-6

    Article  Google Scholar 

  48. Liu Y, Xiong Z (2022) A generalized stochastic resonance based instantaneous frequency estimation method under low SNR. Mechan Syst Signal Process 164:108269. https://doi.org/10.1016/j.ymssp.2021.108269

    Article  Google Scholar 

  49. Mitaim S, Kosko B (1999) Adaptive stochastic resonance. Proc IEEE 86(11):2152–2183. https://doi.org/10.1109/5.726785

    Article  Google Scholar 

  50. Liu Y, Li JT, Feng KP, Zhao YL, Ma H (2020) A novel fault diagnosis method for rotor rub-impact based on nonlinear output frequency response functions and stochastic resonance. J Sound Vib 481:115421. https://doi.org/10.1016/j.jsv.2020.115421

    Article  Google Scholar 

  51. Xin Y, Yong L (1999) Evolutionary programming made faster. IEEE trans evolut comput 3(2):82–102. https://doi.org/10.1109/4235.771163

    Article  Google Scholar 

  52. Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171–188. https://doi.org/10.1007/s00521-017-2988-6

    Article  Google Scholar 

  53. Liang JJ, Suganthan PN, Deb K, editors (2005) Novel composition test functions for numerical global optimization.In: Proceedings 2005 IEEE Swarm Intelligence Symposium. https://doi.org/10.1109/SIS.2005.1501604

  54. Ao Y-c, Shi Y-b, Wei Z, Yan-jun L (2014) Improved particle swarm optimization with adaptive inertia weight. J Univ Electron Sci Technol China 43(6):874–880. https://doi.org/10.3969/j.issn.1001-0548.2014.06.014

    Article  Google Scholar 

  55. Wei F, Jun S, Zhen-Ping X, Wen-Bo X (2010) Convergence analysis of quantum-behaved particle swarm optimization algorithm and study on its control parameter. Acta Phys Sin 59(06):3686–3694. https://doi.org/10.7498/aps.59.3686

    Article  CAS  Google Scholar 

  56. Dehghani M, Trojovský P (2022) Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications. Sci Rep. https://doi.org/10.1038/s41598-022-09514-0

    Article  PubMed  PubMed Central  Google Scholar 

  57. Srinivas M, Patnaik LM (2002) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667. https://doi.org/10.1109/21.286385

    Article  Google Scholar 

  58. Zhang G, Jiang C, Zhang T (2020) A adaptive stochastic resonance method based on two-dimensional tristable controllable system and its application in bearing fault diagnosis. IEEE Access 8:173710–173722. https://doi.org/10.1109/ACCESS.2020.3022803

    Article  Google Scholar 

  59. Cheng W, Xu X, Ding Y, Sun K, Li QQ, Dong L (2020) An adaptive smooth unsaturated bistable stochastic resonance system and its application in rolling bearing fault diagnosis. Chin J Phys 65:629–641. https://doi.org/10.1016/j.cjph.2020.03.015

    Article  MathSciNet  Google Scholar 

  60. Wang H, Chen J, Zhou Y, Ni G (2020) Early fault diagnosis of rolling bearing based on noise-assisted signal feature enhancement and stochastic resonance for intelligent manufacturing. Int J Adv Manuf Technol 107:1017–1023. https://doi.org/10.1007/s00170-019-04333-6

    Article  Google Scholar 

  61. Huang D, Yang J, Zhou D, Litak G (2019) Novel adaptive search method for bearing fault frequency using stochastic resonance quantified by amplitude-domain index. IEEE Trans Instrum Measurement 69:1–13

    Google Scholar 

  62. Liu J, Leng Y, Lai Z, Fan S (2018) Multi-frequency signal detection based on frequency exchange and re-scaling stochastic resonance and its application to weak fault diagnosis. Sensors 18(5):1325–1344. https://doi.org/10.3390/s18051325

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  63. Liu J, Leng Y, Lai Z, Tan D (2016) Stochastic resonance based on frequency information exchange. Acta Phys Sin 65(22):197–210

    Google Scholar 

  64. Case Western Reserve University BDC. [(accessed 19 April 2022)]. https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures/

  65. Wang B, Lei Y, Li N, Li N (2018) A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Trans Reliabil. https://doi.org/10.1109/TR.2018.2882682

    Article  Google Scholar 

  66. Nectoux P, Gouriveau R, Medjaher K, Ramasso E, Varnier C, editors. (2012) PRONOSTIA: An experimental platform for bearings accelerated degradation tests.In: IEEE International Conference on Prognostics and Health Management. https://www.researchgate.net/publication/258028751

  67. Yong L, Rui Y, Tao W, Hewenxuan L, Gangbing S (2018) Health degradation monitoring and early fault diagnosis of a rolling bearing based on CEEMDAN and improved MMSE. Materials 11(6):1009. https://doi.org/10.3390/ma11061009

    Article  Google Scholar 

  68. Li X, Jiang H, Xiong X, Shao H (2019) Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network. Mechan Machine Theory 133:229–249. https://doi.org/10.1016/j.mechmachtheory.2018.11.005

    Article  Google Scholar 

  69. Zhongmin XIE, Chao HU (2023) Application of improved fish swarm algorithm in fault diagnosis of rolling bearing. Machin Design Manufact. https://doi.org/10.19356/j.cnki.1001-3997.20230818.001

    Article  Google Scholar 

  70. Han X, Cao Y, Luan J et al (2023) A rolling bearing fault diagnosis method based on switchable normalization and a deep convolutional neural network. Machines 11(2):185. https://doi.org/10.3390/machines11020185

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region [Grant no. 2022B01017, 2022D01C36 and 2021B01003-1].

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Xinjiang University, Urumqi, 830017, China

    Manhua Yu, Hong Jiang, Jianxing Zhou, Xiangfeng Zhang & Jun Li

Authors
  1. Manhua Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianxing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangfeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MY: Investigation, Methodology, Software, Original draft, Experiments of the algorithms. HJ: Funding acquisition, Investigation, Supervision. JZ: Resources, Formal analysis, Review. XZ: Project administration, Conceptualization. JL: Visualization, Editing.

Corresponding author

Correspondence to Hong Jiang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This research did not contain any studies involving animal or human participants, nor did it take place in any private or protected areas.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, M., Jiang, H., Zhou, J. et al. An improved social mimic optimization algorithm and its application in bearing fault diagnosis. Neural Comput & Applic 36, 7295–7326 (2024). https://doi.org/10.1007/s00521-024-09461-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-024-09461-z

Keywords

Search

Navigation