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Damage Assessment of Rolling Element Bearing Using Cyclostationary Processing of AE Signals with Electromagnetic Interference

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Part of the book series: Applied Condition Monitoring ((ACM,volume 3))

Abstract

Use of acoustic emissions (AE) has been shown to be of aid for bearing damage detection. Rolling element bearings with localized faults produce transient AE activity formed by bursts that repeat in an apparently periodic way. However, the produced signal is actually not periodic, but rather pure cyclostationary at the second order. Since AE are typically measured on the bearing housing, there is high attenuation of the waves, because of the dry metal–metal contact between the outer ring and the housing. In addition, AE instrumentation can be very sensitive to contamination by electromagnetic sources, such as frequency converters usually found near the measurement zones. Both situations combine and create adverse conditions for analysis. In this work a situation such as the described is presented. The measured AE is a corrupted signal where the valuable information is both weak and hidden. A filtering method by means of cyclostationary tools is presented and contrasted with other known methods. The proposed method is used subsequently for defect size estimation.

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References

  1. Achembach, J. D. (1987). Wave propagation in elastic solids. Amsterdam: North-Holland Publishing Company.

    Google Scholar 

  2. Al-Dossary, S., Raja-Hamzah, R. I., & Mba, D. (2006). Acoustic emission waveform changes for varying seeded defect sizes. Advanced Materials Research, 13–14, 427–432.

    Google Scholar 

  3. Al-Ghamdi, A. M., Zhechkov, D., & Mba, D. (2004). The use of acoustic emission for bearing defect identification and estimation of defect size. Lecture 38 EWGAE.

    Google Scholar 

  4. Antoni, J. (2006). The Spectral Kurtosis: a useful tool for characterising nonstationary signals. Mechanical Systems and Signal Processing, 20(2), 282–307.

    Article  Google Scholar 

  5. Antoni, J. (2007). Fast computation of the Kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 21(1), 108–124.

    Article  Google Scholar 

  6. Balderston, H. L. (1969). The detection of incipient failure in bearings. Materials Evaluation, 27, 121–128.

    Google Scholar 

  7. Barszcz, T., & Jablonski, A. (2011). A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing, 25(1), 431–451.

    Article  Google Scholar 

  8. Catlin, J. B. (1983). The use of ultrasonic diagnostic technique to detect rolling element bearing defects. In Proceeding of Machinery and Vibration Monitoring and Analysis Meeting, Vibration Institute, USA, pp. 123–130

    Google Scholar 

  9. Davis, J. L. (2000). Mathematics in wave propagation. Princeton: Princeton University Press.

    Google Scholar 

  10. Holroyd, T. (2000). Acoustic emission and ultrasonics. Machine and systems condition monitoring series. Oxford: Coxmoor Publishing Company.

    Google Scholar 

  11. Ho, D., & Randall, R. B. (2000). Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mechanical Systems and Signal Processing, 14(5), 763–788.

    Article  Google Scholar 

  12. Matthews, J. R. (1983). Acoustic emission. New York: Gordon and Breach Science Publishers Inc.

    Google Scholar 

  13. McFadden, P. D., & Smith, J. D. (1984). Model for the vibration produced by a single point defect in a rolling element bearing. Journal of Sound and Vibration, 96(1), 69–82.

    Google Scholar 

  14. Obuchowski, J., Wyłomańska, A., & Zimroz, R. (2014). Selection of informative frequency band in local damage detection in rotating machinery. Mechanical Systems and Signal Processing, 48(1–2), 138–152.

    Article  Google Scholar 

  15. Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520.

    Google Scholar 

  16. Sawalhi, N., Randall, R. B., & Endo, H. (2007). The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral Kurtosis. Mechanical Systems and Signal Processing, 21(6), 2616–2633.

    Article  Google Scholar 

  17. Shiroishi, J., Li, Y., Liang, S., Kurfess, T., & Danyluk, S. (1997). Bearing condition diagnostics via vibration and acoustic emission measurements. Mechanical Systems and Signal Processing, 11(5), 693–705.

    Article  Google Scholar 

  18. Tallian, T. E. (1992). Failure Atlas for Hertz contact machine elements. New York: ASME.

    Google Scholar 

  19. Vicuña, C. M., & Quezada, D. (2014). Cyclostationary processing of vibration and acoustic emissions for machine failure diagnosis. Cyclostationarity: Theory and methods. Berlin: Springer.

    Google Scholar 

  20. Yoshioka, T., & Fujiwara, T. (1984). Application of acoustic emission technique to detection of rolling bearing failure (Vol. 14, pp. 55–76). New York: ASME Production Engineering Division.

    Google Scholar 

  21. Zhang, Y., & Randall, R. B. (2009). Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast Kurtogram. Mechanical Systems and Signal Processing, 23(5), 1509–1517.

    Article  Google Scholar 

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Correspondence to Cristián Molina Vicuña .

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Acuña, D.Q., Vicuña, C.M. (2015). Damage Assessment of Rolling Element Bearing Using Cyclostationary Processing of AE Signals with Electromagnetic Interference. In: Chaari, F., Leskow, J., Napolitano, A., Zimroz, R., Wylomanska, A., Dudek, A. (eds) Cyclostationarity: Theory and Methods - II. CSTA 2014. Applied Condition Monitoring, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-16330-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-16330-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16329-1

  • Online ISBN: 978-3-319-16330-7

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