Advertisement

Detection of Broken Impeller in Submersible Pump by Estimation of Rotational Frequency from Motor Current Signal

  • Prasanta Kumar PradhanEmail author
  • S. K. Roy
  • A. R. Mohanty
Original Paper
  • 5 Downloads

Abstract

Introduction

With the growth of automation in industry, it has become necessary to monitor the condition of machines and machinery systems to avoid sudden failures which may be catastrophic. There are various condition monitoring techniques available such as wear debris analysis, thermography, vibration analysis, motor current signature analysis (MCSA), etc. for fault detection of machines. Among these, MCSA is a new and emerging technique for fault detection of a submersible pump and other machineries. Apart from proper signal selection, the proper signal processing technique is also important for machinery fault diagnosis. A number of signal processing techniques such as FFT, STFT, and CWT are used for detection of faults by various researchers. In presence of fault, the speed of rotor changes. That will be hidden in the current signal in the form of modulation. Hence, instantaneous frequency (IF) estimation technique is an important technique, which can extract the hidden information.

Purpose

The present work attempts to detect the broken impeller in submersible pump using MCSA.

Methods

Zero-crossing technique and frequency-domain-based IF estimation technique has been adopted to estimate IF from motor current signal and that has been used to detect the fault in submersible pump.

Result and Conclusions

Using these techniques, it is observed that the speed fluctuation increases when there is a defect or level of defect increases in impeller.

Keywords

Condition monitoring Submersible pump Motor current signature analysis (MCSA) Instantaneous frequency Zero-crossing technique (ZCT) 

Notes

References

  1. 1.
    Gabor T (2009) Electrical submersible pumps manual: design, operations, and maintenance. Gulf Professional Publishing, HoustonGoogle Scholar
  2. 2.
    Kar C, Mohanty AR (2006) Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mech Syst Signal Process 20(1):158–187Google Scholar
  3. 3.
    Mohanty AR, Kar C (2006) Fault detection in a multistage gearbox by demodulation of motor current waveform. IEEE Trans Ind Electron 53(4):1285–1297Google Scholar
  4. 4.
    Schoen R, Habetle T, Kamrani F, Bartheld R (1995) Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl 31(6):1274–1279Google Scholar
  5. 5.
    Calıs H, Cakır A (2007) Rotor bar fault diagnosis in three phase induction motors by monitoring fluctuations of motor current zero crossing instants. Electr Power Syst Res 77:385–392Google Scholar
  6. 6.
    Çalıs H, Çakir A (2008) Experimental study for sensor less broken bar detection in induction motors. Energy Convers Manag 49:854–862Google Scholar
  7. 7.
    Thomson WT, Leonard RA, Milne AJ (1984) Failure identification of offshore induction motor systems using on-condition monitoring. Reliab Eng 9:49–64Google Scholar
  8. 8.
    Kliman GB, Stein J, Endicott R (1988) Non evasive detection of broken rotor bars operating induction motors. IEEE Trans Energy Convers 3(4):873–879Google Scholar
  9. 9.
    Weidong L, Chris KM (2006) Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods. J Vib Control 12(2):165–188zbMATHGoogle Scholar
  10. 10.
    Sottile J Jr, Kohler JL (1993) An on-line method to detect incipient failure of turn insulation in random-wound motors. IEEE Trans Energy Convers 8(4):762–768Google Scholar
  11. 11.
    Lee Sang-Bin, Tallam RM, Habetler TG (2003) A robust, on-line turn-fault detection technique for induction machines based on monitoring the sequence component impedance matrix. IEEE Trans Power Electron 18(3):865–872Google Scholar
  12. 12.
    Dorrell DG, Thomson WT, Roach S (1997) Analysis of air gap flux, current, and vibration signals as a function of the combination of static and dynamic air gap eccentricity in 3-phase induction motors. IEEE Trans Ind Appl 33(1):24–34Google Scholar
  13. 13.
    Thomson WT, Barbour A (1998) On-line current monitoring and application of a finite element method to predict the level of static air gap eccentricity in three-phase induction motors. IEEE Trans Energy Convers 13(4):347–357Google Scholar
  14. 14.
    Mc Fadden PD (1986) Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration. J Vib Acoust Stress Reliab Des 108:165–170Google Scholar
  15. 15.
    Cohen L (1995) Time-frequency analysis. Prentice Hall Inc, New JerseyGoogle Scholar
  16. 16.
    Hammod JK, White PR (1996) The analysis of non-stationary signals using time-frequency methods. J Sound Vib 190(3):419–447Google Scholar
  17. 17.
    Strang G, Nguyen T (1996) Wavelet and filter banks. Wellesley-Cambridge Press, CambridgezbMATHGoogle Scholar
  18. 18.
    Mallat SG (1989) A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693zbMATHGoogle Scholar
  19. 19.
    Rodriguez-Donate C et al (2010) Wavelet-based general methodology for multiple fault detection on induction motors at the startup vibration transient. J Vib Control 17(9):1299–1309Google Scholar
  20. 20.
    Fan XF, Zuo MJ (2006) Gearbox fault detection using Hilbert and wavelet packet transform. Mech Syst Signal Process 20(4):966–982Google Scholar
  21. 21.
    Xinhua S, Ran W, Quntao C, Hua S (2014) Cutting sound signal processing for tool breakage detection in face milling based on empirical mode decomposition and independent component analysis. J Vib Control 21(6):3348–3358Google Scholar
  22. 22.
    Ghanberian MM et al (2007) Rotor speed estimation using zero-crossing time signal of stator current. In: Proceeding of the 26th Chinese control conference, 26–31 July 2007, Hunan, China. IEEE, pp 298–302Google Scholar
  23. 23.
    Yang JG et al (2001) Fault detection in a diesel engine by analyzing the instantaneous angular speed. Mech Syst Signal Process 15:549–564Google Scholar
  24. 24.
    Sasi AYB et al (2004) Instantaneous angular speed monitoring of electric motors. J Qual Maint Eng 10:123–135Google Scholar
  25. 25.
    Gu F, Yesilyurt I, Li Y, Harris G, Ball A (2006) An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis. Mech Syst Signal Process 20:1444–1460Google Scholar
  26. 26.
    Stander CJ, Heyns PS (2006) Instantaneous angular speed monitoring of gearboxes under non-cyclic stationary load conditions. Mech Syst Signal Process 19:817–835Google Scholar
  27. 27.
    Charles P et al (2009) Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis. J Sound Vib 321:1171–1185Google Scholar
  28. 28.
    Charles P et al (2010) Application of novel polar representation method for monitoring minor engine condition variations. Mech Syst Signal Process 24:841–843Google Scholar
  29. 29.
    Renaudin et al (2010) Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Meas Sci Technol 24(7):1998–2011Google Scholar
  30. 30.
    Zhou Y, Tao T, Mei X, Jiang G, Sun N (2011) Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method. Mech Syst Signal Process 27:785–793Google Scholar
  31. 31.
    Roy SK, Mohanty AR, Kumar CS (2016) Fault detection in a multistage gearbox by time synchronous averaging of the instantaneous angular speed. J Vib Control 22(2):468–480Google Scholar
  32. 32.
    Gubran AA, Sinha JK (2014) Shaft instantaneous angular speed for blade vibration in rotating machine. Mech Syst Signal Process 44:47–59Google Scholar
  33. 33.
    Lamraoui MM et al (2014) Indicators for monitoring chatter in milling based on instantaneous angular speeds. Mech Syst Signal Process 44:72–85Google Scholar
  34. 34.
    Roy SK, Mohanty AR, Kumar CS (2015) Amplitude demodulation of instantaneous angular speed for fault detection in multistage gearbox. Vib Eng Technol Mach Mech Mach Sci 23:951–961Google Scholar
  35. 35.
    Vakman D (2000) New high precision frequency measurement. Meas Sci Technol 11:1493–1497Google Scholar

Copyright information

© Krishtel eMaging Solutions Private Limited 2019

Authors and Affiliations

  • Prasanta Kumar Pradhan
    • 1
    Email author
  • S. K. Roy
    • 1
  • A. R. Mohanty
    • 1
  1. 1.Mechanical Engineering DepartmentIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations