Artificial neural networks and genetic algorithm for bearing fault detection |
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Authors: | B Samanta K R Al-Balushi S A Al-Araimi |
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Affiliation: | (1) Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, PO Box 33, PC 123 Muscat, Sultanate of Oman |
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Abstract: | A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron
(MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are
extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective
bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal
or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers
and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions.
The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of
a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show
the effectiveness of the features and the classifiers in detection of machine condition. |
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Keywords: | Condition monitoring Feature selection Genetic algorithm Bearing faults Neural network Probabilistic neural network Radial basis function Rotating machines Signal processing |
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