Vibration-based cavitation detection in centrifugal pumps

• Autorzy: Hajnayeb A. , Azizi R. , Ghanbarzadeh A. , Changizian M.
• Języki publikacji: EN

Abstrakty

EN

Cavitation is a common cause of failure in centrifugal pumps. Because of interaction of several mechanical parts and fluid, the vibration signal of a centrifugal pump is complicated. In this paper, the vibrations of a transparent-casing centrifugal pump are studied. Three states are studied experimentally: no cavitation, limited cavitation and developed cavitation. Each case was also confirmed by visually inspecting the cavitation bubbles. The vibrations of the pump was acquired by using an accelerometer that was attached to the casing. Discrete wavelet transform (DWT) analysis and empirical mode decomposition (EMD) are used to extract classification features from the acquired signals. Using these features, an artificial neural network (ANN) successfully diagnosed the cavitation condition of the pump. Finally, EEMD is also implemented. The results showed the success of EMD and DWT in cavitation diagnosis. The output of EEMD does not show significant change comparing to EMD.

Słowa kluczowe

EN

cavitation severity; centrifugal pump; vibration analysis; EEMD; EMD; DWT

PL

kawitacja; pompa odśrodkowa; analiza drgań

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2017
• Tom: Vol. 18, No. 3
• Strony: 77--83
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Hajnayeb A.

• Mechanical Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, P.O. Box 61357-43337, Iran

autor

Azizi R.

• Mechanical Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, P.O. Box 61357-43337, Iran

autor

Ghanbarzadeh A.

• Mechanical Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, P.O. Box 61357-43337, Iran

autor

Changizian M.

• Mechanical Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, P.O. Box 61357-43337, Iran

Bibliografia

• 1. McKee KK, et al. A vibration cavitation sensitivity parameter based on spectral and statistical methods. Expert Systems with Applications, 2015; 42(1):67-78.
• 2. Čudina M, Prezelj J. Detection of cavitation in operation of kinetic pumps. Use of discrete frequency tone in audible spectra. Applied Acoustics, 2009; 70(4): 540-546.
• 3. Černetič J, Prezelj J, Čudina M. Use of noise and vibration signal for detection and monitoring of cavitation in kinetic pumps. The Journal of the Acoustical Society of America, 2008; 123(5): 3316-3316.
• 4. Wang H, Chen P. Fault diagnosis of centrifugal pump using symptom parameters in frequency domain. 2007.
• 5. Tan CZ, Leong MS. An experimental study of cavitation detection in a centrifugal pump using envelope analysis. Journal of System Design and Dynamics, 2008; 2(1): 274-285.
• 6. Sakthivel N, Sugumaran V, Babudevasenapati S. Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications, 2010; 37(6): 4040-4049.
• 7. Černetič J, Čudina M. Estimating uncertainty of measurements for cavitation detection in a centrifugal pump. Measurement, 2011; 44(7): 1293-1299.
• 8. Muralidharan V, Sugumaran V. Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump. Measurement, 2013; 46(1): 353-359.
• 9. Azizi R, et al. Improving accuracy of cavitation severity detection in centrifugal pumps using a hybrid feature selection technique. Measurement, 2017; 108: 9-17.
• 10. Amarnath M, Krishna IP. Detection and diagnosis of surface wear failure in a spur geared system using EEMD based vibration signal analysis. Tribology international, 2013; 61: 224-234. 11.
• Jiang H, Li C, Li H. An improved EEMD with multiwavelet packet for rotating machinery multifault diagnosis. Mechanical Systems and Signal Processing, 2013; 36(2): 225-239.
• 12. Lei Y, He Z, Zi Y. EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Systems with Applications, 2011; 38(6): 7334-7341.
• 13. Wang H, Chen J, Dong G. Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. Mechanical Systems and Signal Processing, 2014; 48(1): 103-119.
• 14. Dybała J. Zimroz R. Empirical mode decomposition of vibration signal for detection of local disturbances in planetary gearbox used in heavy machinery system. Key Engineering Materials. 2014.
• 15. Dybała J. Zimroz R. Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Applied Acoustics, 2014; 77: 195-203.
• 16. Huang NE, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 1998. The Royal Society.
• 17. Yang Y, Yu D, Cheng J. A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement, 2007; 40(9): 943-950.
• 18. Lei Y, He Z, Zi Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2009; 23(4): 1327-1338.
• 19. Gao RX. Yan R. Wavelets: Theory and applications for manufacturing. 2010: Springer Science & Business Media.