4.5 Article

Using MLP-GABP and SVM with wavelet packet transform-based feature extraction for fault diagnosis of a centrifugal pump

Journal

ENERGY SCIENCE & ENGINEERING
Volume 10, Issue 6, Pages 1826-1839

Publisher

WILEY
DOI: 10.1002/ese3.933

Keywords

back propagation (BP); centrifugal pump; genetic algorithm (GA); multilayer feedforward perceptron (MLP); support vector machine (SVM); wavelet packet transform (WPT)

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This study explores artificial intelligent training schemes based on multilayer perceptron with back propagation and genetic algorithm. The hybrid scheme is compared with traditional support vector machine approach for fault and normal scenario analysis of a centrifugal pump. The results show the effectiveness of the proposed hybrid artificial intelligent scheme with optimized hidden layers and neurons.
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 contributions like GA-based selection of hidden layers and neuron, applied in neural network of centrifugal pump condition classification. Furthermore, a hybrid training method combining GA and back propagation (BP) algorithms has been applied for condition classification of a centrifugal pump. The obtained results have shown the good ability of the proposed methods and algorithms.

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