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Using MLP‐GABP and SVM with wavelet packet transform‐based feature extraction for fault diagnosis of a centrifugal pump by Al Tobi, Maamar (author);Bevan, Geraint (author);Wallace, Peter (author);Harrison, David (author);Okedu, Kenneth Eloghene (author) is a Engineering article available to read on EtoBox.

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## Abstract This paper explores artificial intelligent training schemes based on multilayer perceptron, considering back propagation and genetic algorithm (GA). The hybrid scheme is compared with the traditional support vector machine approach in the literature to analyze both fault and normal scenarios of a centrifugal pump. A comparative analysis of the performance of the variables was carried out using both schemes. The study used features extracted for three decomposition levels based on wavelet packet transform. In order to investigate the effectiveness of the extracted features, two mother wavelets were investigated. The salient part of this work is the optimization of the hidden layers numbers using GA. Furthermore, this optimization process was extended to the multilayer perceptron neurons. The evaluation of the model system performance used for the study shows better response of the extracted features, and hidden layers variables including the selected neurons. Moreover, the applied training algorithm used in the work was able to enhance the classifications obtained considering the hybrid artificial intelligent scheme been proposed. This work has achieved a number of contr

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Author

Al Tobi, Maamar (author);Bevan, Geraint (author);Wallace, Peter (author);Harrison, David (author);Okedu, Kenneth Eloghene (author)

Publisher

Wiley

Published

2022

Language

EN

Field

Engineering (Physical Sciences)