SpringerLink
Log in

A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis

基于精细复合多尺度模糊熵与粒子群优化支持向量机的滚动轴承故障诊断

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Combining refined composite multiscale fuzzy entropy (RCMFE) and support vector machine (SVM) with particle swarm optimization (PSO) for diagnosing roller bearing faults is proposed in this paper. Compared with refined composite multiscale sample entropy (RCMSE) and multiscale fuzzy entropy (MFE), the smoothness of RCMFE is superior to that of those models. The corresponding comparison of smoothness and analysis of validity through decomposition accuracy are considered in the numerical experiments by considering the white and 1/f noise signals. Then RCMFE, RCMSE and MFE are developed to affect extraction by using different roller bearing vibration signals. Then the extracted RCMFE, RCMSE and MFE eigenvectors are regarded as the input of the PSO-SVM to diagnose the roller bearing fault. Finally, the results show that the smoothness of RCMFE is superior to that of RCMSE and MFE. Meanwhile, the fault classification accuracy is higher than that of RCMSE and MFE.

摘要

提出了一种结合精细复合多尺度模糊熵和采用粒子群优化支持向量机滚动轴承故障诊断模型。 通过使用白噪声和 1/f 噪声的数值仿真实验中比较**滑度和分解精度的有效性, 与精细复合多尺度样 本熵和多尺度模糊熵相比, 精细复合多尺度模糊熵的**滑度优于这些模型。随后使用精细复合多尺度 模糊熵, 精细复合多尺度样本熵和多尺度模糊熵对不同状态的滚动轴承振动信号进行特征提取, 将提 取的特征向量作为粒子群优化的支持向量机的输入实现滚动轴承故障诊断。实验结果表明, 精细复合 多尺度模糊熵的**滑度优于精细复合多尺度样本熵和多尺度模糊熵, 同时, 故障分类精度高于精细复 合多尺度样本熵和多尺度模糊熵。

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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. HUANG Yi, WANG Bin-xing, WANG Jia-qian. Test for active control of boom vibration of a concrete pump truck [J]. Journal of Vibration and Shock, 2012, 31(2): 1–94. (in Chinese)

    Google Scholar 

  2. RESTA F, RIPAMONTI F, CAZZLUANI G, FERRARI M. Independent modal control for nonlinear flexible structures: An experimental test rig [J]. Journal of Sound and Vibration, 2011, 329(8): 1–972.

    Google Scholar 

  3. BAGORDO G, CAZZLUANI G, RESTA F, RIPAMONTI F. A modal disturbance estimator for vibration suppression in nonlinear flexible structures [J]. Journal of Sound and Vibration, 2011, 330(25): 1–6069.

    Article  Google Scholar 

  4. WANG **ang-bing, TONG Shui-guang. Nonlinear dynamical behavior analysis on rigid flexible coupling mechanical arm of hydraulic excavator [J]. Journal of Vibration and Shock, 2014, 33(1): 1–70. (in Chinese)

    Google Scholar 

  5. WANG D. Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients [J]. Mechanical Systems and Signal Processing, 2018, 108: 1–368.

    Article  Google Scholar 

  6. WANG D. Spectral L2/L1 norm: A new perspective for spectral kurtosis for characterizing non-stationary signals [J]. Mechanical Systems and Signal Processing, 2018, 104: 1–293.

    Article  Google Scholar 

  7. WANG D, ZHAO Y, CAI Y, TUSI K L, LIN J H. Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings [J]. Mechanical Systems and Signal Processing, 2018, 101: 1–308.

    Article  Google Scholar 

  8. PINCUS S M. Approximate entropy as a measure of system complexity [J]. Proceedings of the National Academy of Sciences, 1991, 55: 1–2301.

    MathSciNet  Google Scholar 

  9. YAN R Q, GAO R X. Approximate Entropy as a diagnostic tool for machine health monitoring [J]. Mechanical Systems and Signal Processing, 2007, 21: 1–839.

    Article  Google Scholar 

  10. RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy [J]. American Journal of Physiology∓Heart Circulatory Physiology, 2000, 278(6): 1–2049.

    Article  Google Scholar 

  11. ZHU K H, SONG X, XUE D X. Fault diagnosis of rolling bearings based on IMF envelope sample entropy and support vector machine [J]. Journal of Information & Computational Science, 2013, 10(16): 1–5198.

    Article  Google Scholar 

  12. CHEN W T, ZHUANG J, YU W X, WANG Z Z. Measuring complexity using FuzzyEn, ApEn, and SampEn [J]. Med Eng Phys, 2009, 31: 1–68.

    Article  Google Scholar 

  13. XIONG G L, ZHANG L, LIU H S, ZOU H J, GUO W Z. A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction [J]. J Zhejiang Univ Sci A, 2010, 11(4): 1–279.

    Article  MATH  Google Scholar 

  14. COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of complex physiologic time series [J]. Physical Review Letters, 2002, 89(6): 1–18.

    Article  Google Scholar 

  15. COSTA M, GOLDBERGER A L, PENG C K. Multiscale entropy analysis of biological signals [J]. Physical Review E, 2005, 71(5): 1–18.

    Article  MathSciNet  Google Scholar 

  16. ZHENG **-de, CHENG Jun-sheng, YANG-Yu. A rolling bearing fault diagnosis approach based on multisclae entropy [J]. Journal of Hunan University (Natural Sciences), 2012, 39(5): 1–41. (in Chinese)

    Google Scholar 

  17. ZHANG L, XIONG G L, LIU H S, ZOU H J, GUO W Z. Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference [J]. Expert Systems with Applications, 2010, 37: 1–6085.

    Article  Google Scholar 

  18. ZHENG J D, CHENG J S, YANG Y, LUO S R. A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination [J]. Mechanism and Machine Theory, 2014, 78: 1–200.

    Article  Google Scholar 

  19. ZHENG J D, CHENG J S, YANG Y. A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy [J]. Mechanism and Machine Theory, 2013, 70: 1–453.

    Article  Google Scholar 

  20. WU S D, WU C W, LIN S, G LEE K Y, PENG C K. Analysis of complex time series using refined composite multiscale entropy [J]. Phys Lett A, 2014, 378(20): 1–1374.

    Article  MathSciNet  MATH  Google Scholar 

  21. LIU Z W, CAO H R, CHEN X F, HE Z J, SHEN Z J. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings [J]. Neurocomputing, 2013, 99: 1–410.

    Article  Google Scholar 

  22. DENG W, YAO R, ZHAO H M, YANG X H, LI G Y. A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm [J]. Soft Computing, 2019, 23(7): 1–2462.

    Article  Google Scholar 

  23. DENG W, ZHAO H M, ZOU L, LI G, YANG X H, WU D Q. A novel collaborative optimization algorithm in solving complex optimization problems [J]. Soft Computing, 2017, 21(15): 1–4398.

    Article  Google Scholar 

  24. DENG W, ZHAO H M, LIU J J, YAN X L, LI Y Y, YIN L F. An improved CACO algorithm based on adaptive method and multi-variant strategies [J]. Soft Computing, 2015, 19(3): 1–713.

    Article  Google Scholar 

  25. ZHENG **-de, CHENG Jun-sheng, YANG-Yu. Multi-scale permutation entropy and its applications to rolling bearing fault diagnosis [J]. China Mechanical Engineering, 2013, 24: 1–2646. (in Chinese)

    Google Scholar 

  26. HAN M H, PAN J L. A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings [J]. Measurement, 2015, 24: 1–19.

    Google Scholar 

  27. EBERHART R, KENNEDY J. A new optimizer using particle swarm theory [C]// Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan. 1995: 1–43.

    Google Scholar 

  28. DENG W, ZHAO H M AND YANG X H, XIONG J X, SUN M, LI B. Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment [J]. Applied Soft Computing, 2017, 59: 1–302.

    Article  Google Scholar 

  29. DENG W, CHEN R, HE B, YIN L, GUO J. A novel two-stage hybrid swarm intelligence optimization algorithm and application [J]. Soft Computing, 2012, 16: 1–1722.

    Article  Google Scholar 

  30. GU B, SHENG V S. A robust regularization path algorithm for í-support vector classification [J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(5): 1–1248.

    Article  Google Scholar 

  31. Case Western Reserve University. Bearing data center test seeded fault test data [EB/OL]. 2013. https://doi.org/csegroups.case.edu/bearingdatacenter/pages/download-data-file.

  32. DENG W, ZHANG S J, ZHAO H M, YANG X H. A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing [J]. IEEE Access, 2018, 6: 1–35056.

    Article  Google Scholar 

  33. ZHAO H M, SUN M, WU D, YANG X H. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing [J]. Entropy, 2017, 19(1): 1–21.

    Google Scholar 

  34. TANG R L, LI X, LAI J G. A novel optimal energymanagement strategy for a maritime hybrid energy system based on largescale global optimization [J]. Applied Energy, 2018, 228: 1–264.

    Article  Google Scholar 

  35. TANG R L, WU Z, LI X. Optimal operation of photovoltaic/battery/diesel/coldironing hybrid energy system for maritimeapplication [J]. Energy, 2018, 162: 1–714.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, 990777, China

    Fan Xu  (许凡) & Peter W. Tse  (谢伟达)

Authors
  1. Fan Xu  (许凡)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter W. Tse  (谢伟达)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter W. Tse  (谢伟达).

Additional information

Foundation item: Projects(CityU 11201315, T32-101/15-R) supported by the Research Grants Council of the Hong Kong Special Administrative Region, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, F., Tse, P.W. A method combining refined composite multiscale fuzzy entropy with PSO-SVM for roller bearing fault diagnosis. J. Cent. South Univ. 26, 2404–2417 (2019). https://doi.org/10.1007/s11771-019-4183-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-019-4183-7

Key words

关键词

Search

Navigation