Experimental time-domain vibration- based fault diagnosis of centrifugal pumps using support vector machine

被引：33

作者：

Rapur J.S. ^{[1
]}

Tiwari R. ^{[1
]}

机构：

[1] Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati

来源：

Tiwari, Rajiv (rtiwari@iitg.ernet.in) | 2017年 / American Society of Mechanical Engineers (ASME), United States卷 / 03期

关键词：

Blockage; Centrifugal pump impeller fault; Fault diagnosis; Flow instability; Multidistinct and multicoexisting faults; Support vector machine; Time domain vibration data;

D O I：

10.1115/1.4035440

中图分类号：

学科分类号：

摘要：

When the hydraulic flow path is incompatible with the physical contours of the centrifugal pump (CP), flow instabilities occur. A prolonged operation in the flow-instability region may result in severe damages of the CP system. Hence, two of the major causes of flow instabilities such as the suction blockage (with five levels of increasing severity) and impeller defects are studied in the present work. Thereafter, an attempt is made to classify these faults and differentiate the physics behind the flow instabilities caused due to them. The tri-axial CP vibration data in time domain are employed for the fault classification. Multidistinct and multicoexisting fault classifications have been performed with different combinations of these faults using support vector machine (SVM) algorithm with radial basis function (RBF) kernel. Prediction results from the experiments and the developed methodology help to segregate the faults into appropriate class, identify the severity of the suction blockage, and substantiate the practical applicability of this study. Copyright © 2017 by ASME.

引用

共 17 条

[1]

Ugechi C.I., Ogbonnaya E.A., Lilly M.T., Ogaji S.O.T., Probert S.D., Condition-based diagnostic approach for predicting the maintenance requirements of machinery, Engineering, 1, 3, pp. 177-187, (2009)

[2]

Alfayez L., Mba D., Dyson G., The application of acoustic emission for detecting incipient cavitation and the best efficiency point of a 60 kw centrifugal pump: Case study, NDT e Int, 38, 5, pp. 354-358, (2005)

[3]

Sakthivel N.R., Sugumaran V., Nair B.B., Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono- block centrifugal pump, Mech. Syst. Signal Process, 24, 6, pp. 1887-1906, (2010)

[4]

Harihara P.P., Parlos A.G., Sensorless detection of impeller cracks in motor driven centrifugal pumps, ASME Paper No. IMECE2008-66273

[5]

Sakthivel N.R., Sugumaran V., Babudevasenapati S., Vibration based fault diagnosis of monoblock centrifugal pump using decision tree, Expert Syst. Appl, 37, 6, pp. 4040-4049, (2010)

[6]

Rajakarunakaran S., Venkumar P., Devaraj D., Rao K.S.P., Artificial neural network approach for fault detection in rotary system, Appl. Soft Comput, 8, 1, pp. 740-748, (2008)

[7]

Farokhzad S., Vibration based fault detection of centrifugal pump by fast fourier transform and adaptive neuro-fuzzy inference system, J. Mech. Eng. Technol, 1, 3, pp. 82-87, (2013)

[8]

Vladimir V.N., Vapnik V., The Nature of Statistical Learning Theory, (1995)

[9]

Samanta B., Al-Balushi K.R., Al-Araimi S.A., Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Eng. Appl. Artif. Intell, 16, 7-8, pp. 657-665, (2003)

[10]

Yin Z., Hou J., Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes, Neurocomputing, 174, pp. 643-650, (2016)

← 1 2 →