Experimental Set-Up for Investigation of Fault Diagnosis of a Centrifugal Pump

Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

Abstract:

Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: Centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128949

References:

[1] Beebe, R. S. (2004) Predictive maintenance of pumps using condition monitoring Elsevier advanced technology.
[2] Mckee, K. K., Forbes, G., Mazhar, I., Entwistle, R. & Howard, I. (2011) A review of major centrifugal pump failure modes with application to the water supply and sewerage industries. ICOMS Asset Management Conference. Gold Coast, QLD, Australia, Asset Management Council.
[3] Rao, B. K. N. (Ed.) (1996) Handbook of condition monitoring, Elsevier advanced technology.
[4] Bendjama, H., Gherfi, K., Idiou, D. & Boucherit, M. S. (2014) Condition monitoring of rotating machinery by vibration signal processing methods. International Conference on Industrial Engineering and Manufacturing. Batna University Algeria.
[5] Aherwar, A. & Khalid, M. S. (2012) Vibration Analysis Techniques for Gearbox Diagnostic: a Review. International Journal of Advanced Engineering Technology, 3.
[6] Al-Tubi, M. A. S. & Al-Raheem, K. F. (2010) Rolling element bearing faults detection, a time domain analysis. Caledonian Journal of Engineering, 6.
[7] Al-Tubi, M. A. S., Al-Raheem, K. F. & Abdul-Karem, W. (2012) Rolling element bearing element faults detection, power spectrum and envelope analysis. International conference on applications and design in mechanical engineering. Penang, Malaysia.
[8] Mehala, N. & Dahiya, R. (2008) A Comparative Study of FFT, STFT and Wavelet Techniques for Induction Machine Fault Diagnostic Analysis. International conference of computational intelligence, man-machine systems cybernetics (CIMMACS '08). India.
[9] Prakash, A., Agarwal, V. K., Kumar, A. & Nand, B. (2014) A review on machine condition monitoring and fault diagnostics using wavelet transform. International Journal of Engineering Technology, Management and Applied Sciences, 2, 84-93.
[10] Peng, Z. K. & Chu, F. L. (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing, 18, 199-221.
[11] Al-Tubi, M. A. S. & Al-Raheem, K. F. (2015) Rotor misalignment and imbalance detection using wavelet and neural network techniques. Scottish Journal of arts, social sciences and scientific studies, 24, 33-44.
[12] Muralidharan, V., Sugumaran, V. & Indira, V. (2014) Fault diagnosis of monoblock centrifugal pump using SVM. Engineering Science and Technology, an International Journal, 17, 152e157.
[13] Yan, R., Gao, R. X. & Chen, X. (2014) Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96, 1-15.
[14] Farokhzad, S. (2013) Vibration based fault detection of centrifugal pump by fast fourier transform and adaptive neuro-fuzzy inference system. Journal of mechanical engineering and technology, 1, 82-87.
[15] Rajakarunakaran, S., Venkumar, P., Devaraj, D. & Rao, K. S. P. (2008) Artificial neural network approach for fault detection in rotary system. Applied Soft Computing, 8, 740–748.
[16] Sakthivel, N. R., Nair, B. B., Elangovan, M., Sugumaran, V. & Saravanmurugan, S. (2014) Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals. Engineering Science and Technology, an International Journal 17, 30-38.
[17] Charniak, E. & Mcdermott, D. (2000) Introduction to artificial intelligence, addison wesley Longman Inc.
[18] Mcculloch, W. S. & Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133.
[19] Zadeh, L. A. (1965) Fuzzy Sets. Information and Control, 8, 338-353.
[20] Sakthivel, N. R., V. Sugumaran & B.NAIR, B. (2012) Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump. Applied Soft Computing, 12, 196–203.
[21] Sakthivel, N. R., Binoy. B. Nair & V. Sugumaran (2012) Soft computing approach to fault diagnosis of centrifugal pump. Applied Soft Computing, 12, 1574–1581.
[22] Saberi, M., Azadeh, A., Nourmohammadzadeh, A. & Pazhoheshfar, P. (2011) Comparing performance and robustness of SVM and ANN for fault diagnosis in a centrifugal pump. 19th International Congress on Modelling and Simulation. Perth, Australia.
[23] Nasiri, M. R., Mahjoob, M. J. & Vahid-Alizadeh, H. (2011) Vibartion signature analysis for detecting cavitation in centrifugal pump using neural networks. IEEE international conference on mechatronics (ICM). Istanbul, Turkey, IEEE.
[24] Farokhzad, S., Ahmadi, H. & Jafary, A. (2013) FAULT Classification of Centrifugal Water Pump Based on Decision TREE and Regression Model. Journal of Science and today's world, 2, 170-176.
[25] Farokhzad, S., Ahmadi, H. & Jaefari, A. (2012) Artificial Neural Network Based Classification of Faults in Centrifugal Water Pump. Journal Vibroengineering 14.
[26] Muralidharan, V. & Sugumaran, V. (2013) Selection of Discrete Wavelets for Fault Diagnosis of Monoblock Centrifugal Pump Using the J48 Algorithm. Applied Artificial Intelligence, 27.
[27] Wang, H. & Chen, P. (2007) Intelligent Method for Condition Diagnosis of Pump System Using Discrete Wavelet Transform, Rough Sets and Neural Network. Second international Conference on Bio-inspired computing: theories and applications, 2007. Zhengzhou, IEEE.
[28] Muralidharan, V., Sugumaran, V., Shanmugam, P. & Sivanathan, K. (2010) Artifical neural network based classification for monoblock centrifugal pump using wavelet analysis. International journal of mechanical engineering, 1, 28-37.
[29] Iiott, P. W. & Griffiths, A. J. (1997) Fault diagnosis of pumping machinery using artificial neural networks. Journal of Process Mechanical Engineering, 211, 185-194.
[30] Zouari, R., Sieg-Zieba, S. & SIDAHMED, M. (2004) Fault detection system for centrifugal pumps using neural networks and neuro-fuzzy techniques. Surveillance 5 CETIM Senlis.
[31] Song, L., Chen, P. & Wang, H. (2014) Automatic Decision Method of Optimum Symptom Parameters and Frequency Bands for Intelligent Machinery Diagnosis: Application to Condition Diagnosis of Centrifugal Pump System. Advances in Mechanical Engineering.
[32] Al-Braik, A., Hamomd, O., Gu, F. & Ball, A. D. (2014) Diagnosis of Impeller Faults in a Centrifugal Pump Based on Spectrum Analysis of Vibration Signals. Eleventh International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. Manchester UK.