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Journal of Mechanical Science and Technology

, Volume 33, Issue 4, pp 1513–1522 | Cite as

Early fault detection method for rolling bearing based on multiscale morphological filtering of information-entropy threshold

  • Lingli CuiEmail author
  • Jialong Wang
  • Jianfeng Ma
Article
  • 41 Downloads

Abstract

The scale of structure element is especially important to obtain good filtering results in multiscale morphological filtering (MMF) method. In general, the optimal scale of structure element is set to be a fixed value in traditional morphological filter, therefore it is difficult to extract the fault feature from rolling bearing vibration signal effectively. A novel multiscale morphological filtering algorithm is proposed based on information-entropy threshold (IET-MMF) for early fault detection of rolling bearing. Compared with traditional MMF method, several optimal scales of structure elements are achieved according to the energy distribution characteristic of different vibration signals. The information entropy theory is applied to quantify the analyzed signals, and the optimal threshold of information entropy is obtained by iterative algorithm to ensure integrity of useful information. The simulation and rolling bearing experimental analysis results show that the IET-MMF method can extract fault features of vibration signals effectively.

Keywords

Multiscale morphological filtering Information entropy Feature extraction Rolling bearing 

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References

  1. [1]
    C. Q. Shen, Y. M. Qi, J. Wang, G. G. Cai and Z. K. Zhu, An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive, Engineering Applications of Artificial Intelligence, 76 (2018)170–184.CrossRefGoogle Scholar
  2. [2]
    L. Y. Song, H. Q. Wang and P. Chen, Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery, IEEE Transactions on Instrumentation and Measurement, 67 (8) (2018) 1887–1899.CrossRefGoogle Scholar
  3. [3]
    L. L. Cui, J. F. Huang and F. B. Zhang, Quantitative and localization diagnosis of a defective ball bearing based on vertical-horizontal synchronization signal analysis, IEEE Transactions on Industrial Electronics, 64 (11) (2017) 8695–8705.CrossRefGoogle Scholar
  4. [4]
    H. Q. Wang, P. X. Wang, L. Y. Song, B. Y. Ren and L. L. Cui, A novel feature enhancement method based on improved constraint model of online dictionary learning, IEEE Access, 7 (2019) 17599–17607.CrossRefGoogle Scholar
  5. [5]
    L. Y. Song, H. Q. Wang and P. Chen, Step-by-step fuzzy diagnosis method for equipment based on symptom extraction and trivalent logic fuzzy diagnosis theory, IEEE Transactions on Fuzzy Systems, 26 (6) (2018) 3467–3478.CrossRefGoogle Scholar
  6. [6]
    A. J. Hu and L. Xiang, An optimal selection method for morphological filter’s parameters and its application in bearing fault diagnosis, Journal of Mechanical Science and Technology, 30 (3) (2016) 1055–1063.MathSciNetCrossRefGoogle Scholar
  7. [7]
    L. L. Cui, B. B. Li, J. F. Ma and Z. Jin, Quantitative trend fault diagnosis of a rolling bearing based on Sparsogram and Lempel-Ziv, Measurement, 128 (2018) 410–418.CrossRefGoogle Scholar
  8. [8]
    J. Serra, Morphological filtering: An overview, Signal Processing, 38 (1) (1994) 3–11.MathSciNetCrossRefzbMATHGoogle Scholar
  9. [9]
    J. Serra and L. Vincent, An overview of morphological filtering, Circuits Systems and Signal Processing, 11 (1) (1992) 47–108.MathSciNetCrossRefzbMATHGoogle Scholar
  10. [10]
    Y. F. Li, X. H. Liang and M. J. Zuo, Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis, Mechanical Systems and Signal Processing, 85 (2017) 146–161.CrossRefGoogle Scholar
  11. [11]
    L. J. Meng, J. W. Xiang, Y. T. Zhong and W. L. Song, Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter, Journal of Mechanical Science and Technology, 29 (8) (2015) 3121–3129.CrossRefGoogle Scholar
  12. [12]
    Y. B. Dong, M. F. Liao, X. L. Zhang and F. Z. Wang, Faults diagnosis of rolling element bearings based on modified morphological method, Mechanical Systems and Signal Processing, 25 (4) (2011) 1276–1286.CrossRefGoogle Scholar
  13. [13]
    J. Wang, G. H. Xu, Q. Zhang and L. Liang, Application of improved morphological filter to the extraction of impulsive attenuation signals, Mechanical Systems and Signal Processing, 23 (1) (2009) 236–245.CrossRefGoogle Scholar
  14. [14]
    L. L. Cui, J. Wang and S. Lee, Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis, Journal of Sound and Vibration, 333 (10) (2014) 2840–2862.CrossRefGoogle Scholar
  15. [15]
    B. Li, P. L. Zhang, Z. J. Wang, S. S. Mi and Y. T. Zhang, Gear fault detection using multi-scale morphological filters, Measurement, 44 (10) (2011) 2078–2089.CrossRefGoogle Scholar
  16. [16]
    W. L. Jiang, Z. Zheng, Y. Zhu and Y. Li, Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis, Mechanical Systems and Signal Processing, 58–59 (2015) 179–205.Google Scholar
  17. [17]
    N. G. Nikolaou and I. A. Antoniadis, Application of morphological operators as envelope extractors for impulsive-type periodic signals, Mechanical Systems and Signal Processing, 17 (6) (2003) 1147–1162.CrossRefGoogle Scholar
  18. [18]
    A. S. Raj and N. Murali, Early classification of bearing faults using morphological operators and fuzzy inference, IEEE Transactions on Industrial Electronics, 60 (2) (2013) 567–574.CrossRefGoogle Scholar
  19. [19]
    L. L. Cui, J. F. Huang, F. B. Zhang and F. L. Chu, HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault, Mechanical Systems and Signal Processing, 120 (1) (2019) 608–629.CrossRefGoogle Scholar
  20. [20]
    J. B. Gao, F. Y. Liu, J. F. Zhang, J. Hu and Y. H. Cao, Information entropy as a basic building block of complexity theory, Entropy, 15 (9) (2013) 3396–3418.MathSciNetCrossRefzbMATHGoogle Scholar
  21. [21]
    Y. T. Ai, J. Y. Guan, C. W. Fei, J. Tian and F. L. Zhang, Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance, Mechanical Systems and Signal Processing, 88 (2017) 123–136.CrossRefGoogle Scholar
  22. [22]
    P. Maragos and R. W. Schafer, Morphological filters 1. Their set-theoretic analysis and relations to linear shift-invariant filters, IEEE Transactions on Acoustics Speech and Signal Processing, 35 (8) (1987) 1153–1169.MathSciNetCrossRefGoogle Scholar
  23. [23]
    J. Wang, Q. Zhang and G. H. Xu, Extraction of operation characteristics in mechanical systems using genetic morphological filter, Journal of Vibroengineering, 15 (1) (2013) 185–195.Google Scholar
  24. [24]
    L. J. Zhang, J. W. Xu, H. H. Yang, D. Yang and D. D. Wang, Multiscale morphology analysis and its application to fault diagnosis, Mechanical Systems and Signal Processing, 22 (3) (2008) 597–610.CrossRefGoogle Scholar
  25. [25]
    Y. B. Dong, M. F. Liao, X. L. Zhang and F. Z. Wang, Faults diagnosis of rolling element bearings based on modified morphological method, Mechanical Systems and Signal Processing, 25 (4) (2011) 1276–1286.CrossRefGoogle Scholar
  26. [26]

Copyright information

© KSME & Springer 2019

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Manufacturing TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Engineering Research Center of Precision Measurement Technology and InstrumentsBeijing University of TechnologyBeijingChina

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