Faults diagnosis of a centrifugal pump using multilayer perceptron genetic algorithm back propagation and support vector machine with discrete wavelet transform‐based feature extraction

This paper presents a comparative study of two artificial intelligent systems, namely; Multilayer Perceptron (MLP) and support vector machine (SVM), to classify six fault conditions and the normal (nonfaulty) condition of a centrifugal pump. A hybrid training method for MLP is proposed for this work...

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Bibliographic Details
Published inComputational intelligence Vol. 37; no. 1; pp. 21 - 46
Main Authors Al Tobi, Maamar, Bevan, Geraint, Wallace, Peter, Harrison, David, Okedu, Kenneth Eloghene
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.02.2021
Blackwell Publishing Ltd
Subjects
Back propagation
centrifugal pump
Centrifugal pumps
Classification
Comparative studies
Discrete Wavelet Transform
Fault diagnosis
Feature extraction
genetic algorithm
Genetic algorithms
multilayer perceptron
Multilayer perceptrons
Neurons
Support vector machines
Wave propagation
Wavelet analysis
Wavelet transforms
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Summary:This paper presents a comparative study of two artificial intelligent systems, namely; Multilayer Perceptron (MLP) and support vector machine (SVM), to classify six fault conditions and the normal (nonfaulty) condition of a centrifugal pump. A hybrid training method for MLP is proposed for this work based on the combination of Back Propagation (BP) and Genetic Algorithm (GA). The two training algorithms are tested and compared separately as well. Features are extracted using Discrete Wavelet Transform (DWT), both approximations, details, and two mother wavelets were used to investigate their effectiveness on feature extraction. GA is also used to optimize the number of hidden layers and neurons of MLP. In this study, the feature extraction, GA‐based hidden layers, neurons selection, training algorithm, and classification performance, based on the strengths and weaknesses of each method, are discussed. From the results obtained, it is observed that the DWT with both MLP‐BP and SVM produces better classification rates and performances.
ISSN:0824-7935
1467-8640
DOI:10.1111/coin.12390