Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volume 35, Issue 5, Pages 5391-5402Publisher
IOS PRESS
DOI: 10.3233/JIFS-169821
Keywords
Rolling bearing; signal processing; IMFs; empirical mode decomposition (EMD); fault detection; artificial neural networks (ANNs); MLPs; PNN; GRNN; RBF; LVQ
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Rolling bearing is an important mechanical element therefore its condition monitoring is necessary to ensure the steadiness of industrial machineries. In this paper, the open source vibration data have been processed using advanced signal processing techniques such as EMD method to extract more symmetric waves (IMFs) out of non-linear and non-stationary vibration signals. In addition to this, statistical time-domain and frequency-domain features are calculated and then J48 Decision Tree Algorithm is used for feature selection. The processed input signals have been used for comparative study of five different types of Artificial Neural Network (ANN) classifiers. The performance characteristics of MLP, PNN, GRNN, RBF and LVQ are shown in results and discussion section.
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