期刊
NONLINEAR DYNAMICS
卷 102, 期 3, 页码 1717-1731出版社
SPRINGER
DOI: 10.1007/s11071-020-06014-6
关键词
Permutation entropy; Convolutional neural network; Feature extraction; Fault detection
资金
- Research, Development and Innovation Fund of Kaunas University of Technology
- International Science and technology cooperation project [BZ2018022]
Bearing fault diagnosis based on the classification of patterns of permutation entropy is presented in this paper. Patterns of permutation entropy are constructed by using non-uniform embedding of the vibration signal into a delay coordinate space with variable time lags. These patterns are interpreted, processed and classified by employing deep learning techniques based on convolutional neural networks. Computational experiments are used to compare the accuracy of classification with other methods and to demonstrate the efficacy of the presented early defect detection and classification method.
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