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Technical Brief

Experimental Time-Domain Vibration-Based Fault Diagnosis of Centrifugal Pumps Using Support Vector Machine

[+] Author and Article Information
Janani Shruti Rapur

Department of Mechanical Engineering,
Indian Institute of Technology Guwahati,
Guwahati 781039, India
e-mail: shruti.rapur@gmail.com

Rajiv Tiwari

Professor
Department of Mechanical Engineering,
Indian Institute of Technology Guwahati,
Guwahati 781039, India
e-mail: rtiwari@iitg.ernet.in

1Corresponding author.

Manuscript received May 26, 2016; final manuscript received November 24, 2016; published online June 13, 2017. Assoc. Editor: Mohammad Pourgol-Mohammad.

ASME J. Risk Uncertainty Part B 3(4), 044501 (Jun 13, 2017) (7 pages) Paper No: RISK-16-1087; doi: 10.1115/1.4035440 History: Received May 26, 2016; Revised November 24, 2016

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.

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Figures

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Fig. 1

(Left) Experimental setup and (Right) mounted accelerometers

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Fig. 2

Pump with faulty impeller

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Fig. 3

Recirculation in the pump—a schematic representation

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Fig. 4

Data processing flowchart

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Fig. 5

Speed versus classification accuracy of different features, case D, B1

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Fig. 6

Blockage level versus average classification accuracy over entire speed range, case D

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Fig. 7

Blockage level versus average classification accuracy over entire speed range, case E

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Fig. 8

Individual classification accuracy for each fault at different speeds for ((i)) B1, (ii) B2, (iii) B3, and (iv) B4; with (μ–σ–S) feature, case F

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Fig. 9

Classification accuracy by testing with one fault at a time for different speeds with (μ–σ–S) feature, case G

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