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

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.