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

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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.

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References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  8. J. Serra, Morphological filtering: An overview, Signal Processing, 38 (1) (1994) 3–11.

    Article  MathSciNet  MATH  Google Scholar 

  9. J. Serra and L. Vincent, An overview of morphological filtering, Circuits Systems and Signal Processing, 11 (1) (1992) 47–108.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  26. https://doi.org/csegroups.case.edu/bearingdatacneter/home.

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China

    Lingli Cui & Jialong Wang

  2. Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing, 100124, China

    Jianfeng Ma

Authors
  1. Lingli Cui
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  2. Jialong Wang
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  3. Jianfeng Ma
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Corresponding author

Correspondence to Lingli Cui.

Additional information

Recommended by Associate Editor Kyoung-Su Park

Lingli Cui received the B.S. degree in mechanical engineering from Shenyang Aerospace University, Shenyang, China, in 1998, the M.S. degree in mechanical engineering and automation from Harbin Institute of Technology, Harbin, China, in 2001, and the Ph.D. degree in control theory and control engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 2004. She is currently a Professor of mechanical engineering, Beijing University of Technology, Beijing, China. Her research interests include fault mechanism, pattern recognition, intelligent diagnosis, and fault diagnosis.

Jialong Wang received the B.S. degree in mechanical engineering from Tangshan College, Tangshan, China, in 2016. He is currently pursuing the M.S. degree in mechanical engineering at Beijing University of Technology, Beijing, China. His research interests include rolling element bearing fault diagnosis.

Jianfeng Ma received the Ph.D. degree in Beihang University, Beijing, China, in 2009. Now he works at Beijing University of Technology. His current research interests include mechanism dynamics, fault diagnosis, light weighting and structural bionics.

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Cui, L., Wang, J. & Ma, J. Early fault detection method for rolling bearing based on multiscale morphological filtering of information-entropy threshold. J Mech Sci Technol 33, 1513–1522 (2019). https://doi.org/10.1007/s12206-019-0303-4

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  • DOI: https://doi.org/10.1007/s12206-019-0303-4

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