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Journal of Failure Analysis and Prevention

, Volume 19, Issue 6, pp 1556–1568 | Cite as

Model-Based Condition Monitoring for the Detection of Failure of a Ball Bearing in a Centrifugal Pump

  • I. M. JamadarEmail author
  • S. A. I. Bellary
  • R. A. Kanai
  • A. A. Alrobaian
Case History---Peer-Reviewed
  • 19 Downloads

Abstract

Centrifugal pumps are employed as important rotating machineries that play a vital role in sugar production plants. Untimely bearing failure, impeller damages, wearing out of sealing system and cavitation are the main faults that occur in centrifugal pump which need to be detected in advance for uninterrupted production activity in sugar plants. In such condition, the rolling bearing damage is a primary indicator of the possible major issues of the centrifugal pumps. The objective of this paper is to report a novel method that has been applied for the detection of the faulty bearing of a centrifugal pump used as a boiler feed pump in sugar production unit. A case study particularly of a deep groove ball bearing used to support non-drive end of pump is presented. Theoretical formulation of response parameters such as vibration velocity and frequency has been carried out using dimensional analysis. Subsequently, the in situ experiments have been performed using multichannel vibration analyzer (Brand—ADASH, Model-A4400 VA4 Pro) for validation.

Keywords

Centrifugal pump Faults Bearing Dimensional analysis 

Notes

Acknowledgments

The authors are extremely thankful to Mr. Pravin Patil, Maintenance Engineer, Rajarambapu Sahakari Sakhar Karkhana Pvt. Ltd. (Sarvodaya Sugar Factory), Karandwadi, Sangli, Maharashtra, India, support staff for providing all necessary help and machinery specification during experimentation.

References

  1. 1.
    A. El-Shafei, T.A.F. Hassan, A.K. Soliman, Y. Zeyada, N. Rieger, Neural network and fuzzy logic diagnostics of 1x faults in rotating machinery. ASME J. Eng. Gas Turbines Power 129, 703–710 (2007)CrossRefGoogle Scholar
  2. 2.
    A. Sofronas, Case Histories in Vibration Analysis and Metal Fatigue for the Practicing Engineer (Wiley, Hobokem, 2012)CrossRefGoogle Scholar
  3. 3.
    N.R. Sakthivel, B.B. Nair, M. Elangovan, V. Sugumaran, S. Saravanmurugan, Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals. Elsiver Eng. Sci. Technol. Int. J. 17, 30–38 (2014)Google Scholar
  4. 4.
    Z. Ma, S. Wang, J. Shi, T. Li, X. Wang, Fault diagnosis of an intelligent hydraulic pump based on a nonlinear unknown input observer. Elsevier Chin. J. Aeronaut. (2017).  https://doi.org/10.1016/j.cja.2017.05.004 CrossRefGoogle Scholar
  5. 5.
    V. Muralidharan, V. Sugumaran, V. Indira, Fault diagnosis of monoblock centrifugal pump using SVM. Elsiver Eng. Sci. Technol. Int. J. 17, 152–157 (2014)Google Scholar
  6. 6.
    A. Kumar, R. Kumar, Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump. Elsevier J. Meas. (2017).  https://doi.org/10.1016/j.measurement.2017.04.041 CrossRefGoogle Scholar
  7. 7.
    A. Adamkowski, A. Henke, M. Lewandowski, Resonance of torsional vibrations of centrifugal pump shafts due to cavitation erosion of pump impellers. Elsevier J. Eng. Fail. Anal. 70, 56–72 (2016)CrossRefGoogle Scholar
  8. 8.
    L. Xiang, A. Hu, New feature extraction method for the detection of defects in rolling element bearings. ASME J. Eng. Gas Turbines Power 134, 084501-1–084501-5 (2012)CrossRefGoogle Scholar
  9. 9.
    J.S. Rapur, R. Tiwari, Experimental time-domain vibration based fault diagnosis of centrifugal pumps using SVM. ASME J. Risk Uncertain. Eng. Syst. Part B Mech. Eng. (2016).  https://doi.org/10.1115/1.4035440 CrossRefGoogle Scholar
  10. 10.
    V. Muralidharan, V. Sugumaran, Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump. Elsevier J. Meas. 46, 353–359 (2013)CrossRefGoogle Scholar
  11. 11.
    N.R. Sakthivel, V. Sugumaran, S. Babudevasenapati, Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Elsevier J. Exp. Syst. Appl. 37, 4040–4049 (2010)CrossRefGoogle Scholar
  12. 12.
    M. Zhang, Z. Jiang, K. Feng, Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump. Elsevier J. Mech. Syst. Signal Proc. 93, 460–493 (2017)CrossRefGoogle Scholar
  13. 13.
    H. Wang, P. Chen, Sequential condition diagnosis for centrifugal pump system using fuzzy neural network. Neural Inf. Proc. Lett. Rev. 11, 41–50 (2007)CrossRefGoogle Scholar
  14. 14.
    F. Kong, R. Chen, A combined method for triplexpump fault diagnosis based on wavelet transform, fuzzy logic and neuro-networks. Elsevier J. Mech. Syst. Signal Proc. 18, 161–168 (2004)CrossRefGoogle Scholar
  15. 15.
    J. Wang, H. Hu, Vibration-based fault diagnosis of pump using fuzzy technique. Elsevier J. Meas. 39, 176–185 (2006)CrossRefGoogle Scholar
  16. 16.
    J.C. Gibbings, Dimensional Analysis (Springer, London, 2011)CrossRefGoogle Scholar
  17. 17.
    S. Thomas, Applied Dimensional Analysis and Modeling, 2nd edn. (Butterworth-Heinemann, MA, 2007)Google Scholar
  18. 18.
    I.M. Jamadar, D.P. Vakharia, A numerical model for the identification of the structural damages in rolling contact bearings using matrix method of dimensional analysis. ASME J. Tribol. 1, 1 (2015).  https://doi.org/10.1115/1.4031989 CrossRefGoogle Scholar
  19. 19.
    C. Sujatha, Vibration and acoustics-measurement and signal analysis, 1st edn. (Tata McGraw-Hill, New Delhi, 2010)Google Scholar
  20. 20.
    T.A. Harris, Rolling Bearing Analysis, 5th edn. (Wiley, New York, 1996)Google Scholar
  21. 21.
    C.M. Harris, A.G. Piersol, Harris’ Shock and Vibration Handbook, 5th edn. (McGraw-Hill, New-York, 2002)Google Scholar

Copyright information

© ASM International 2019

Authors and Affiliations

  • I. M. Jamadar
    • 1
    Email author
  • S. A. I. Bellary
    • 1
  • R. A. Kanai
    • 1
  • A. A. Alrobaian
    • 2
  1. 1.Annasaheb Dange College of Engineering and Technology, AshtaWalwa, SangliIndia
  2. 2.Qassim UniversityBuraydahSaudi Arabia

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