Abstract
AbstractThis 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.
Highlights
Various techniques have been applied for fault detection of centrifugal pumps regarding condition monitoring, such as analysis based on time[1] and frequency domain, considering fast Fourier transform (FFT).[2,3]
The strengths and drawbacks of the three considered AI schemes, in relation to the effect possessed by the selection of the mother wavelet, using approximation detail features, and normalized or non-n ormalized features were investigated
When the features were extracted as approximations, enhanced classification rates were achieved than approximation and detail
Summary
Various techniques have been applied for fault detection of centrifugal pumps regarding condition monitoring, such as analysis based on time[1] and frequency domain, considering fast Fourier transform (FFT).[2,3] a technique known as wavelet that is multi-resolution and powerful in nature has been employed for detection of faults in rotating machinery This technique has the capability of non-stationary signal analysis in machines.3-5. ANN has been employed for the automatic classification of fault diagnosis in centrifugal pumps in references.8-10 The main concept of WT is based on presenting both the time and frequency domains, in which it possess the ability to analyze the non-stationary signals that are not easy to be analyzed using Fourier transform It is a multi-p owerful resolution method that requires fast calculation process.[11] Among the different WT methods, the wavelet packet transform (WPT) is one of the promising type that has been applied along with AI in machines fault diagnosis. WPT with db[4] wavelet function is used for the feature extraction, and the classification rates are 82%, 99.3%, and 98.6% for MLP, SVM, and RBF, respectively
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