A Multi-scale Fuzzy Measure Entropy and Infinite Feature Selection Based Approach for Rolling Bearing Fault Diagnosis

  • Keheng ZhuEmail author
  • Liang Chen
  • Xiong Hu


Fuzzy measure entropy (FuzzyMEn) is a recently improved non-linear dynamic parameter for evaluating the signals’ complexity. In comparison with fuzzy entropy (FuzzyEn), which only emphasizes the local characteristics of the signal but neglects its global trend, FuzzyMEn can reflect not only the local but also the global characteristics of the signal. Therefore, by calculating the FuzzyMEn values in different scales, the multi-scale fuzzy measure entropy (MFME) method is put forward in this paper and used for extracting the fault features from vibration signals of rolling bearing. After the feature extraction, the newly developed infinite feature selection (Inf-FS) method is employed to choose the most representative features from the original ones of high dimension. Finally, a new rolling bearing fault diagnosis approach is presented based on MFME, Inf-FS and support vector machine (SVM). The experimental analysis indicates that the presented approach can realize the rolling bearing fault diagnosis effectively.


Multi-scale fuzzy measure entropy Infinite feature selection Bearing fault diagnosis 



  1. 1.
    Tiwari, R., Gupta, V.K., Kankar, P.K.: Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. J. Vib. Control 21, 461–467 (2015)CrossRefGoogle Scholar
  2. 2.
    Kumar, A., Kumar, R.: Role of signal processing, modeling and decision making in the diagnosis of rolling element bearing defect: a review. J. Nondestr. Eval. 38(1), 5 (2019)CrossRefGoogle Scholar
  3. 3.
    Lei, Y.G., Lin, J., He, Z.J., Zi, Y.Y.: Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Signal. Process. 25, 1738–1749 (2011)CrossRefGoogle Scholar
  4. 4.
    Sharma, A., Amarnath, M., Kankar, P.K.: Novel ensemble techniques for classification of rolling element bearing faults. J. Braz. Soc. Mech. Sci. Eng. 39(3), 709–724 (2017)CrossRefGoogle Scholar
  5. 5.
    Dong, G., Chen, J., Zhao, F.: Incipient bearing fault feature extraction based on minimum entropy deconvolution and k-singular value decomposition. J. Manuf. Sci. Eng. 139(10), 101006 (2017)CrossRefGoogle Scholar
  6. 6.
    Vakharia, V., Gupta, V.K., Kankar, P.K.: Efficient fault diagnosis of ball bearing using ReliefF and Random Forest classifier. J. Braz. Soc. Mech. Sci. Eng. 39(8), 2969–2982 (2017)CrossRefGoogle Scholar
  7. 7.
    Feng, Z., Chen, X.: Adaptive iterative generalized demodulation for nonstationary complex signal analysis: principle and application in rotating machinery fault diagnosis. Mech. Syst. Signal. Process. 110, 1–27 (2018)CrossRefGoogle Scholar
  8. 8.
    Yang, Y., Yu, D.J., Cheng, J.S.: A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J. Sound Vib. 294, 269–277 (2006)CrossRefGoogle Scholar
  9. 9.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. U.S.A. 88(6), 2297–2301 (1991)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Yan, R., Gao, R.X.: Approximate entropy as a diagnostic tool for machine health monitoring. Mech. Syst. Signal. Process. 21, 241–250 (2007)Google Scholar
  11. 11.
    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)CrossRefGoogle Scholar
  12. 12.
    Chen, W.T., Wang, Z.Z., Xie, H.B., Yu, W.: Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans. Neural Syst. Rehabil. Eng. 15(2), 266–272 (2007)CrossRefGoogle Scholar
  13. 13.
    Chen, W.T., Zhuang, J., Yu, W.: Measuring complexity using FuzzyEn, ApEn and SampEn. Med. Eng. Phys. 31, 61–68 (2009)CrossRefGoogle Scholar
  14. 14.
    Zheng, J.D., Cheng, J.S., Yang, Y.: A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy. Mech. Mach. Theory 70, 441–453 (2013)CrossRefGoogle Scholar
  15. 15.
    Zhu, K.H., Li, H.L.: A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine. Proc. IMechE Part C J. Mech. Eng. Sci. 230(13), 2314–2322 (2016)CrossRefGoogle Scholar
  16. 16.
    Liu, C.Y., Li, K., Zhao, L.N., Liu, F., Zheng, D., Liu, C., Liu, S.: Analysis of heart variability using fuzzy measure entropy. Comput. Biol. Med. 43, 100–108 (2013)CrossRefGoogle Scholar
  17. 17.
    Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of physiologic time series. Phys. Rev. Lett. 89, 062102 (2002)CrossRefGoogle Scholar
  18. 18.
    Costa, M., Goldberger, A.L., Peng, C.K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71, 021906 (2005)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Liu, H.H., Han, M.H.: A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings. Mech. Mach. Theory 75, 67–78 (2014)CrossRefGoogle Scholar
  20. 20.
    Zhang, L., Xiong, G.L., Liu, H.S.: Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst. Appl. 37, 6017–6085 (2010)Google Scholar
  21. 21.
    Zheng, J.D., Cheng, J.S., Yang, Y., Luo, S.: A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination. Mech. Mach. Theory 78, 187–200 (2014)CrossRefGoogle Scholar
  22. 22.
    Li, Y.B., Xu, M.Q., Wang, R.X., Huang, W.: A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy. J. Sound Vib. 360, 277–299 (2016)CrossRefGoogle Scholar
  23. 23.
    Cerrada, M., Sánchez, R.V., Pacheco, F., Cabrera, D., Zurita, G., Li, C.: Hierarchical feature selection based on relative dependency for gear fault diagnosis. Appl. Intell. 44(3), 687–703 (2016)CrossRefGoogle Scholar
  24. 24.
    Giorgio, R., Melzi, S., Cristani, M.: Infinite feature selection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4202–4210 (2015)Google Scholar
  25. 25.
    Zhu, K.H., Song, X.G., Xue, D.X.: A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement 47, 669–675 (2014)CrossRefGoogle Scholar
  26. 26.
    Rapur, J.S., Tiwari, R.: On-line time domain vibration and current signals based multi-fault diagnosis of centrifugal pumps using support vector machines. J. Nondestr. Eval. 38(1), 6 (2018)CrossRefGoogle Scholar
  27. 27.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefGoogle Scholar
  28. 28.
    Lou, X., Loparo, K.A.: Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech. Syst. Signal. Process. 18(5), 1077–1095 (2004)CrossRefGoogle Scholar
  29. 29.
    Widodo, A., Yang, B.S.: Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal. Process. 21(6), 2560–2574 (2007)CrossRefGoogle Scholar
  30. 30.
    He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. Adv. Neural. Inf. Process. Syst. 18, 507–514 (2005)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Logistics EngineeringShanghai Maritime UniversityShanghaiChina
  2. 2.College of Energy and Power EngineeringDalian University of TechnologyDalianChina

Personalised recommendations