Identification of Cavitation Phenomenon in Centrifugal Pump by Artificial Immune Network Method

Document Type : Research Article

Authors

1 PhD student/IUST

2 IUST

Abstract

Reduce the cost of unscheduled shutdown and enhance the reliability of systems, is one of the important goals for various industries that could be achieved by condition monitoring. Cavitation is a common phenomenon in centrifugal pumps which causes the damage and its true identification in early stage is too important. In this paper cavitation is identified by use of artificial immune net that is modeled on the function of the human immune system. For this purpose, after data collection by a laboratory setup and extraction of various features, feature selection and dimensions reduction were done by artificial immune method and then with artificial immune net method, the system condition was identified. Finally, the results of this study were compared with the principal component analysis method and the results of nonlinear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means clustering.

Keywords

Main Subjects


[1] Čudina, M., DETECTION OF CAVITATION PHENOMENON IN A CENTRIFUGAL PUMP USING AUDIBLE SOUND. Mech. Syst. Signal Process, 2003. 17(6): p. 12.
[2] J. L. Parrondo, S.V., and C. Santolaria,, Development of a predictive maintenance system for a centrifugal pump. Qual. Maint. Eng, 1998. 4(3): p. 13.
[3] Jensen, J., Detecting Cavitation in Centrifugal Pumps Experimental Results of the Pump Laboratory. 2000:p. 4.
[4] Prezelj, M.Č.a.J., Detection of cavitation in operation of kinetic pumps. Use of discrete frequency tone in audible spectra. Appl. Acoust, 2009. 70(4): p. 6.
[5] Li, S.C., Cavitation of Hydraulic Machinery, in Mechanic. 2000, Imperial College Press.
[6] M. R. Nasiri, M.J.M., and H. Vahid-Alizadeh, Vibration Signature Analysis for Detecting Cavitation in Centrifugal Pumps using N eural Networks. 2011:p. 4.
[7] Ph, R.R.a.D., Classification of Vibration Signal to Detect Pump Cavitation using Discrete Wavelet Transform,. Appl.Mech, 2014. 93(10): p. 4.
[8]     Safizadeh,  S.M.a.M.N.N.,  Using vibration signals for cavitaion monitoring in centrifiugal pumps. Aerospace .Mech, 2014. 10(3): p. 9. (In Persian).
[9]    Feldmeier, D.B.D.a.G.R., Predictive versus preventive maintenance - Future control technologies  in  motor  diagnosis   and system wellness -  Future  control technologies in motor diagnosis and system wellness. IEEE Ind. Appl, 2004. 10(5): p. 9.
[10]  Parlos,   P.P.H.a.A.G.   Sensorless Detection  of Cavitation in Centrifugal Pumps. in IMECE. 2006.
[11]  S. Al-Hashmi, F.G., Y. Li, A. D. Ball, Cavitation Detection of a Centrifugal Pump Using Instantanous Angular Speed. Appl.mech, 2004. 3(27): p. 5.
[12]  Farokhzad, S., Vibration Based Fault Detection of Centrifugal Pump by Fast Fourier Transform and Adaptive Neuro- Fuzzy Inference System. Mech .Eng and Tech, 2013. 1(3): p. 5.
[13]  Azadeh,  A.,  et  al,  A  flexible  algorithm for fault diagnosis in a  centrifugal  pump with  corrupteddata and  noise  based  on ANN and support vector machine withhyper-parameters     optimization. Applied Soft Computing, 2013. 1(3): p. 7.
[14]  Farokhnezhad,  S.,  Ahmadi,  H.   and Jafari, A, Intelligent fault detection in centrifugal pump by hybrid method of artificial neural network and wavelet transform. in third International Conference of Industrial Automation. Petrolium Department, 2012. (In Persian).
[15] Laurentys, C.A., R.M. Palhares, and W.M. Caminhas, A novel Artificial Immune System for fault behavior detection. Expert Systems with Applications, 2011. 38: p. 9.
[16]   Silva, G.C., R.M. Palhares, and W.M. Caminhas, Immune inspired  Fault  Detection  and  Diagnosis:  A fuzzy-based approach of the negative selection algorithm and participatory clustering. Expert Systems with Applications, 2012. 39: p. 12.
[17]    Rachid, F.B.a.F., Modeling gaseous and vaporous cavitation in liquidflows within the context of the thermodynamics of irreversible processes. International .Jour of Non-Linear .Mech, 2014. 65: p. 7.
[18]   Jerne, N.K., Towards a Network Theory of the Immune System, in Annals of Immunology. 1974: Newyork-USA. p. 373-378.
[19]  Jiang, W., Chen,Y.,Zhang,J, A fault diagnosis method based on artificial immune network. Applied Mechanics and Material, 2013. 385: p. 4.
[20]   Ehsani, S., Moqadam, A, Reduction of spirituality by artificial immune system. Computer and Robatic, 2008.(In Persian).