Advertisement

Fault Diagnosis of Fuel System Based on Improved Extreme Learning Machine

  • Hairui Wang
  • Wanting JingEmail author
  • Ya Li
  • Hongwei YangEmail author
Article
  • 13 Downloads

Abstract

In this paper, extreme learning machine (ELM) method is used to classify the faults of fuel system. Although the learning speed of ELM is fast, its classification accuracy and generalization ability need to be improved. Bat Algorithm has a strong ability of global optimization. In order to make up for the deficiency of the ELM, this paper proposes a fault diagnosis model based on an improved bat algorithm to optimize the ELM. The experimental results show that the improved bat algorithm greatly improves the classification accuracy and generalization ability of the ELM, and verifies the validity of the proposed model.

Keywords

Extreme learning machine Bat algorithm Fuel system Fault diagnosis 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61263023 and 61863016).

References

  1. 1.
    Jardine AKS, Ralston P, Reid N et al (2010) Proportional hazards analysis of diesel engine failure data. Qual Reliab Eng Int 5(3):207–216CrossRefGoogle Scholar
  2. 2.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRefGoogle Scholar
  3. 3.
    Huang GB, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122CrossRefGoogle Scholar
  4. 4.
    Huang GB, Zhou H, Ding X et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529CrossRefGoogle Scholar
  5. 5.
    Wang Y, Cao F, Yuan Y (2014) A study on effectiveness of extreme learning machine. Neurocomputing 74(16):2483–2490CrossRefGoogle Scholar
  6. 6.
    Tang J, Deng C, Huang GB (2017) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809–821MathSciNetCrossRefGoogle Scholar
  7. 7.
    Luo J, Vong CM, Wong PK (2017) Sparse Bayesian extreme learning machine for multi-classification. IEEE Trans Neural Netw Learn Syst 25(4):836–843Google Scholar
  8. 8.
    Li Z, Ye L, Zhao YN et al (2016) Short-term wind power prediction based on extreme learning machine with error correction. Prot Control Modern Power Syst 1(1):1CrossRefGoogle Scholar
  9. 9.
    Huang G, Huang GB, Song S et al (2015) Trends in extreme learning machines: a review. Neural Netw Off J Int Neural Netw Soc 61(C):32–48CrossRefGoogle Scholar
  10. 10.
    Yang XS (2010) A new metaheuristic Bat-inspired algorithm. Comput Knowl Technol 284:65–74zbMATHGoogle Scholar
  11. 11.
    Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483CrossRefGoogle Scholar
  12. 12.
    Yang X-S (2012) Bat algorithm for multi-objective optimization. Int J Bio-Inspired Comput 3(5):267–274CrossRefGoogle Scholar
  13. 13.
    Yang XS, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141CrossRefGoogle Scholar
  14. 14.
    Yilmaz A, Kucuksille EU (2014) A new modification approach on bat algorithm for solving optimization problems. Appl Soft Comput 28(5):259–275Google Scholar
  15. 15.
    Huang G-B, Chen Y-Q, Babri HA (2000) Classification ability of single hidden layer feedforward neural networks. IEEE Trans Neural Netw 11(3):799–801CrossRefGoogle Scholar
  16. 16.
    Guliyev NJ, Ismailov V (2016) A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function. Neural Comput 28(7):1289–1304MathSciNetCrossRefGoogle Scholar
  17. 17.
    Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062.CrossRefGoogle Scholar
  18. 18.
    Zhu QY, Qin AK, Suganthan PN et al (2005) Rapid and brief communication: evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunmingChina
  2. 2.Yunnan Provincial Education DepartmentKunmingChina

Personalised recommendations