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Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

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An Erratum to this article was published on 30 April 2016

Abstract

Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains. One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment. In this work, we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum (MPS) through a multi-scale morphology analysis procedure. The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves. Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

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References

  1. Yang, G.W., Wei, Y.J., Zhao, G.L., et al.: Current research progress in the mechanics of high speed rails. Adv. Mech. 45, 201510 (2015)

    Google Scholar 

  2. Elforjani, M., Mba, D.: Natural mechanical degradation measurements in slow speed bearings. Eng. Fail. Anal. 16, 521–532 (2009)

    Article  Google Scholar 

  3. Al-Ghamda, A.M., Mba, D.: A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech. Syst. Signal Process. 20, 1537–1571 (2006)

    Article  Google Scholar 

  4. Zhang, L., Xu, J., Yang, J., et al.: Multiscale morphology analysis and its application to fault diagnosis. Mech. Syst. Signal Process. 22, 597–610 (2008)

    Article  Google Scholar 

  5. Hu, A., Tang, G., An, L.: Denoising technique for vibration signals of rotating machinery based on mathematical morphology filter. Chin. J. Mech. Eng. 42, 127–130 (2006)

    Article  Google Scholar 

  6. Hao, R., Feng, Z., Chu, F.: Morphology filters for analyzing roller bearing fault using acoustic emission signal processing. J. Tsinghua Univ. 5, 812–815 (2008)

    Google Scholar 

  7. Zuo, Y., Wang, X., Xu, X.: Application of degree of cyclostation to trend analysis of fault in generator sets. J. Vib. Meas. Diagn. 29, 292–294 (2009)

    Google Scholar 

  8. Wang, N., Lv, F., Liu, Y., et al.: Study on application of adaptive generalized morphological filter in processing online monitoring tan & date. Proc. CSEE. 24, 161–165 (2004)

    Google Scholar 

  9. Zhang, W., Yang, C., Zhou, X.: Application of morphology filtering method in vibration signal denoising. J. Zhejiang Univ. (Eng. Sci.) 43, 2096–2099 (2009)

    Google Scholar 

  10. Lu, Z., Mi, D., Xu, Z.: Research in application of multi-scale and multi-element morphological filtering in geomagnetic orientation. J. Hebei Univ. Sci. Technol. 32, 228–232 (2011)

    Google Scholar 

  11. Li, X., Yang, J., Li, M., et al.: A time-frequency filtering method based on generalized S transform and its application in machinery fault diagnosis. Appl. Mech. Mater. 157, 531–537 (2012)

    Google Scholar 

  12. Dong, Y., Liao, M., Zhang, X., et al.: Faults diagnosis of rolling element bearings based on modified morphological method. Mech. Syst. Signal Process. 2–5, 1276–1286 (2011)

    Article  Google Scholar 

  13. Maragos, P.: Pattern spectrum and multi-scale shape representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 701–716 (1989)

    Article  MATH  Google Scholar 

  14. Hao, R., Lu, W., Chu, F.: Multiscale morphological analysis on fault signals of rolling element bearing Chinese. J. Mech. Eng. 44, 160–165 (2008)

    Article  Google Scholar 

  15. Yu, X., Lu, W., Chu, F.: Fault diagnostics based on pattern spectrum entropy and proximal support vector machine. Key Eng. Mater. 413, 607–612 (2009)

  16. Hao, R., Feng, Z., Chu, F.: Application of support vector machine based on pattern spectrum entropy in fault diagnostics of bearings. In: prognostics & system health management conference (2010)

  17. Zhang, K., Cheng, J., Yang, Y.: Rotating machinery fault diagnosis based on local mean decomposition and pattern spectrum. J. Vib. Shock 32, 135–140 (2013)

    Google Scholar 

  18. Wang, F., Zhang, Y., Zhang, B., et al.: Application of wavelet packet sample entropy in the forecast of rolling element bearing fault trend. International conference on multimedia and signal processing 12–16 (2011)

  19. Zhao, Z., Yang, S.: Sample entropy-based roller bearing fault diagnosis method. J. Vib. Shock 31, 136–154 (2012)

    Google Scholar 

  20. Zhu, K., Song, X., Xue, D.: Fault diagnosis of rolling bearings based on IMF envelope sample entropy and support vector machine. J. Inform. Comput. Sci. 10, 5189–5198 (2013)

    Article  Google Scholar 

  21. Hong, H.B., Liang, M.: Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform. J. Sound Vib. 320, 452–468 (2009)

    Article  Google Scholar 

  22. Zhang, C., Chen, J.: Fault severity assessment for rolling element bearings based on LMD and Lempel-Ziv index. J. Vib. Shock. 31, 77–82 (2012)

    Google Scholar 

  23. Wang, J., Cui, L., Wang, H., et al.: Improved complexity based on time-frequency analysis in bearing quantitative diagnosis. Adv. Mech. Eng. 5, 258506 (2013)

    Article  Google Scholar 

  24. Serra, J.: Image Analysis and Mathematical Morphology. Academic, New York (1982)

    MATH  Google Scholar 

  25. Richman, J.S., Moorman, R.J.: Physiological time series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–2049 (2000)

    Google Scholar 

  26. Lempel, A., Ziv, J.: On the complexity of finite sequences. IEEE Trans. Inf. Theory 22, 75–81 (1976)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant 51205017), the National Science and Technology Support Program (Grant 2015BAG12B01) and the National Basic Research Program of China (Grant 2015CB654805)..

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Authors and Affiliations

  1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Wen-Jing Wang & Dao-Yun Chen

  2. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China

    Ling-Li Cui

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  1. Wen-Jing Wang
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  2. Ling-Li Cui
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  3. Dao-Yun Chen
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Correspondence to Wen-Jing Wang.

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Wang, WJ., Cui, LL. & Chen, DY. Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault. Acta Mech. Sin. 32, 265–272 (2016). https://doi.org/10.1007/s10409-015-0529-z

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  • DOI: https://doi.org/10.1007/s10409-015-0529-z

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