Fault Diagnosis of Monoblock Centrifugal Pump Using Stationary Wavelet Features and Bayes Algorithm

Authors

  • V. Muralidharan Department of Mechanical Engineering, B.S.Abdur Rahman University, Chennai - 600048. Tamil Nadu, India
  • V. Sugumaran V.Sugumaran, School of Mechanical and Building Sciences, VIT University, Chennai, Tamil Nadu, India
  • N. R. Sakthivel 3 N.R. Sakthivel, 3 Department of Mechanical Engineering, Amrita University, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajsat-2014.3.2.797

Abstract

Fault diagnosis of monoblock centrifugal pump is conceived as a pattern recognition problem. There are three important steps to be performed in pattern recognition namely feature extraction, feature selection and classification. In this study, Stationary wavelet transform (SWT) is used for feature extraction from the input signals and Bayes net classifier is used for classification. A WEKA implementation of Bayes net algorithm is used. The different fault conditions considered for the present study are Cavitation (CAV), Impeller fault (FI), Bearing Fault (BF) and both Impeller and Bearing Fault (FBI). The representative signal is acquired for all faulty conditions,
Features are extracted, classified and the results are presented. The experimental setup and the procedure for conducting the experiments are discussed in detail.

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Published

05-11-2014

How to Cite

Muralidharan, V., Sugumaran, V., & Sakthivel, N. R. (2014). Fault Diagnosis of Monoblock Centrifugal Pump Using Stationary Wavelet Features and Bayes Algorithm. Asian Journal of Science and Applied Technology, 3(2), 1–4. https://doi.org/10.51983/ajsat-2014.3.2.797