Skip to main content
SpringerLink
Log in

Condition Monitoring of Naturally Damaged Slewing Bearing Based on EMD and EEMD Methods

  • Conference paper
  • First Online:
Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

  • 1783 Accesses

Abstract

Vibration-based condition monitoring and prognostic of rotating element bearings have been studied. Satisfactory methods on how identify bearing fault and lifetime prediction have been shown in literature. There are two characteristics of the investigated bearings in the literature: (1) The bearings were run in moderate and high rotating speed and (2) damage was artificially introduced, e.g., artificial crack and seeded fault. This paper deals with slewing bearing with very low rotational speed (1 and 4.5 rpm) with natural fault development. Two real vibration data are discussed, namely data obtained from laboratory slewing bearing test-rig and data from a sheet metal company. In this study, the result of EMD and EEMD applied in two real cases were shown superior compared to FFT method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 199.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Slewing Bearings [Product Catalogue] (2007) RotheErde GmbH, Dortmund

    Google Scholar 

  2. Moodie CAS (2009) An investigation into the condition monitoring of large slow speed slew bearings. PhD thesis in faculty of engineering, University of Wollongong, Wollongong

    Google Scholar 

  3. Bai X, Xiao H, Zhang L (2011) The condition monitoring of large slewing bearing based on oil analysis method. Key Eng Mater 474–476:716–719

    Article  Google Scholar 

  4. Liu R (2007) Condition monitoring of low-speed and heavily loaded rolling element bearing. Ind Lubr Tribol 59(6):297–300

    Article  Google Scholar 

  5. Marciniec A, Torstenfelt B (1994) Load distribution in flexibly supported three-row roller slew bearings. Tribol Trans 37(4):757–762

    Article  Google Scholar 

  6. Kania L (2006) Modelling of rollers in calculation of slewing bearing with the use of finite elements. Mech Mach Theory 41(11):1359–1376

    Article  MATH  Google Scholar 

  7. Göncz P, Potočnik R, Glodež S (2011) Load capacity of a three-row roller slewing bearing raceway. Eng Procedia 10:1196–1201

    Article  Google Scholar 

  8. Gang Z, Xue Z, Kaifeng Z, Juan R, Dede J, Qingzhen Y, Mingyan L, Yan Z (2011) Optimization design of large-scale cross-roller slewing bearing used in special propeller. Appl Mech Mater 48–49:787–792

    Google Scholar 

  9. Glodež S, Potočnik R, Flašker J (2012) Computational model for calculation of static capacity and lifetime of large slewing bearing’s raceway. Mech Mach Theory 47:16–30

    Article  Google Scholar 

  10. Aguirrebeitia J, Abasolo M, Avilés R, Fernández de Bustos I (2012) Theoretical calculation of general static load-carrying capacity for the design and selection of three row roller slewing bearings. Mech Mach Theory 48:52–61

    Article  Google Scholar 

  11. Rodger LM (1979) The application of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings. Tribol Int 12:51–59

    Article  Google Scholar 

  12. Liu Z, Chen J (2011) Monitoring and diagnosis technique on slewing bearing. Adv Mater Res 156–157:1478–1483

    Google Scholar 

  13. Žvokelj M, Zupan S, Prebil I (2010) Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis. Mech Sys Sign Proc 24:1049–1067

    Article  Google Scholar 

  14. Žvokelj M, Zupan S, Prebil I (2011) Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method. Mech Sys Sign Proc 25:2631–2653

    Article  Google Scholar 

  15. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454:903–995

    Article  MATH  MathSciNet  Google Scholar 

  16. Guanlei X, Xiaotong W, Xiaogan X (2009) Time-varying frequency-shifting signal-assisted empirical mode decomposition method for AM-FM signals. Mech Sys Sign Proc 23:2458–2469

    Article  Google Scholar 

  17. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41

    Article  Google Scholar 

  18. Wu Z, Huang NE (2005) Ensemble empirical mode decomposition: a noise-assisted data analysis method, centre for ocean-land-atmosphere studies. Technical Report 193:51, http://www.iges.org/pubs/tech.html

  19. Caesarendra W, Park JH, Kosasih PB, Choi BK (2013) Condition monitoring of low speed slewing bearings based on ensemble empirical mode decomposition method. Trans Korean Soc Noise Vib Eng 23(2):131–143

    Article  Google Scholar 

Download references

Acknowledgments

The first author gratefully acknowledges the University of Wollongong financial support through University Postgraduate Award (UPA) and International Postgraduate Tuition Award (IPTA). Also this research was partially supported by the Basic Science Research Program through NRF (No. 2011-0013652).

Author information

Authors and Affiliations

  1. Department of Energy and Mechanical Engineering, Institute of Marine Industry, Gyeongsang National University, 445 Inpyeong-dong, Tongyeong, Gyeongnam-do, 650-160, South Korea

    Wahyu Caesarendra, Buyung Kosasih, Byeong-Keun Choi, Kiet Tieu & Craig A. S. Moodie

Authors
  1. Wahyu Caesarendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Buyung Kosasih
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Byeong-Keun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kiet Tieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Craig A. S. Moodie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Byeong-Keun Choi .

Editor information

Editors and Affiliations

  1. World Congress on Engineering Asset Management, Seoul, Korea, Republic of (South Korea)

    Woo Bang Lee

  2. World Congress on Engineering Asset Management, Gyeongnam, Korea, Republic of (South Korea)

    Byeongkuen Choi

  3. Queensland University of Technology, Graceville, Queensland, Australia

    Lin Ma

  4. Queensland University of Technology, Sunnybank Hills, Queensland, Australia

    Joseph Mathew

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Caesarendra, W., Kosasih, B., Choi, BK., Tieu, K., Moodie, C.A.S. (2015). Condition Monitoring of Naturally Damaged Slewing Bearing Based on EMD and EEMD Methods. In: Lee, W., Choi, B., Ma, L., Mathew, J. (eds) Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012). Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06966-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06966-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02461-5

  • Online ISBN: 978-3-319-06966-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation