MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||6|
|Section||Study of Advanced Materials and Performance Analysis|
|Published online||15 February 2021|
Research on bearing diagnosis technology based on wavelet transform and one-dimensional convolutional neural network
1 Department of Power Engineering, Naval University of Engineering, Wuhan 430000, China
2 Unit 91315, China
* Corresponding authors: firstname.lastname@example.org
Aiming at the fault diagnosis of rolling element bearings, propose a method for fine diagnosis of bearings based on wavelet transform and one-dimensional convolutional neural network. First use wavelet transform to decompose the experimental data; Use the resulting low-frequency signal as a one-dimensional convolutional neural network input, bearing fault identification. The experiment uses the deep groove ball bearing of Case Western Reserve University as the research object, Use this method to identify the normal and outer ring faults of the bearing. the result shows: This method can be effectively applied to the precise identification of bearings.
© The Authors, published by EDP Sciences, 2021
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