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Study on Rotating Machine Vibration Behavior Using Measured Vibro-Acoustic Signals

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Advances in Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO 2014)

Abstract

Literatures have shown that there is a significant rise in the use of measured vibro-acoustic signals for faults diagnosis in rotating machines. This is particularly based on the premise that affluent information about a rotating machine’s operating conditions is usually conveyed by the sounds of the machine. Several earlier studies have already shown the usefulness and capabilities of amplitude spectra for faults diagnosis. However, very limited analyses of rotating machine’s vibro-acoustic signals are available in literatures. Hence, the current study compares the fused amplitude spectra of measured vibration signals from a flexibly supported rotating machine with different faults, using accelerometers and microphones. The experiments, spectra computations and observations are presented here.

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References

  1. Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23

    Article  Google Scholar 

  2. Basir O, Yuan XH (2007) Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Inf Fus 8(4):379–386

    Article  Google Scholar 

  3. Niu G, Han T, Yang BS, Tan ACC (2007) Multi-agent decision fusion for motor fault diagnosis. Mech Syst Signal Process 21(3):1285–1299

    Article  Google Scholar 

  4. Zhang J (2007) Improved on-line process fault diagnosis through information fusion in multiple neural networks. Comput Chem Eng 30(1):558–571

    Google Scholar 

  5. Boutros T, Liang M (2007) Mechanical fault detection using fuzzy index fusion. Int J Mach Tools Manuf 47(11):1702–1714

    Article  Google Scholar 

  6. Niu G, Yang BS, Pecht M (2010) Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance. Reliab Eng Syst Safety 95(7):786–796

    Article  Google Scholar 

  7. Yunusa-Kaltungo A, Sinha JK (2014) An improved data fusion technique for faults diagnosis in rotating machines. Measurement 58:27–32

    Article  Google Scholar 

  8. Elbhbah K, Sinha JK (2013) Vibration-based condition monitoring of rotating machines using a composite spectrum. J Sound Vib 332(11):2831–2845

    Article  Google Scholar 

  9. Wang J, He Q, Kong F (2013) Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis. Mech Syst Signal Process 40(1):237–256

    Article  Google Scholar 

  10. Gubran AA, Sinha JK (2014) Shaft instantaneous angular speed for blade vibration in rotating machine. Mech Syst Signal Process 44(1–2):47–59

    Article  Google Scholar 

  11. Al-Badour F, Sunar M, Cheded L (2011) Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques. Mech Syst Signal Process 25(6):2083–2101

    Article  Google Scholar 

  12. Pennacchi P, Vania A (2008) Diagnostics of a crack in a load coupling of a gas turbine using the machine model and the analysis of the shaft vibrations. Mech Syst Signal Process 22(5):1157–1178

    Article  Google Scholar 

  13. Yunusa-Kaltungo A, Sinha JK (2014) Combined bispectrum and trispectrum for faults diagnosis in rotating machines. Proc Inst Mech Eng Part O J Risk Reliab 228(4):419–428

    Google Scholar 

  14. Kilundu B, Chiementin X, Duez J, Mba D (2011) Cyclostationarity of acoustic emissions (AE) for monitoring bearing defects. Mech Syst Signal Process 25:2061–2072

    Article  Google Scholar 

  15. Tan CK, Irving P, Mba D (2007) A comparative experimental study on the diagnostic and prognostic capabilities of acoustic emission, vibration and spectrometric oil analysis for spur gears. Mech Syst Signal Process 21:208–233

    Article  Google Scholar 

  16. Tandon N, Yadava GS, Ramakrishma KM (2007) A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings. Mech Syst Signal Process 21(1):244–256

    Article  Google Scholar 

  17. Loutas TH, Sotiriades G, Kalaitzoglou I, Kostopoulos V (2009) Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements. Appl Acoust 70:1148–1159

    Article  Google Scholar 

  18. Li W, Parkin RM, Coy J, Gu F (2002) Acoustic based condition monitoring of a diesel engine using self-organising map networks. Appl Acoust 63:699–711

    Article  Google Scholar 

  19. Garcia-Perez A, Romero-Troncoso RJ, Cabal-Yepez E, Osornio-Rios RA, Lucio-Martinez JA (2011) Application of high-resolution spectral analysis for identifying faults in induction motors by means of sound. J Vib Control 18(11):1585–1594

    Article  Google Scholar 

  20. Cernetic J (2009) The use of noise and vibration signals for detecting cavitation in kinetic pumps. Proc Inst Mech Eng Part C J Mech Eng Sci 223:1645–1655

    Article  Google Scholar 

  21. Shibata K, Takahashi A, Shirai T (2000) Fault diagnosis of rotating machinery through visualisation of sound signals. Mech Syst Signal Process 14(2):229–241

    Article  Google Scholar 

  22. Lin J (2001) Feature extraction of machine sound using wavelet and its application in faults diagnosis. NDE&T Int 34:25–30

    Google Scholar 

  23. Loutas TH, Roulias D, Pauly E, Kostopoulos V (2011) The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mech Syst Signal Process 25(4):1339–1352

    Article  Google Scholar 

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Correspondence to Akilu Yunusa-Kaltungo .

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Yunusa-Kaltungo, A., Sinha, J.K., Nembhard, A.D. (2016). Study on Rotating Machine Vibration Behavior Using Measured Vibro-Acoustic Signals. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_33

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  • DOI: https://doi.org/10.1007/978-3-319-20463-5_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20462-8

  • Online ISBN: 978-3-319-20463-5

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