Rolling bearing fault diagnosis based on EEMD sample entropy and PNN

Rolling bearing fault diagnosis based on EEMD sample entropy and PNN

A fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) sample entropy and probabilistic neural network (PNN) is proposed for non-steady and non-linear signals. First, the rolling bearing signals are decomposed into intrinsic mode function (IMF) using EEMD....

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Bibliographic Details
Main Authors: Xiuli Liu, Xueying Zhang, Zhongquan Luan, Xiaoli Xu
Format: Article
Language:English
Published: Wiley 2019-12-01
Series:The Journal of Engineering
Subjects:
fault diagnosis
neural nets
rolling bearings
vibrational signal processing
hilbert transforms
probability
ensemble empirical mode decomposition sample entropy
probabilistic neural network
kurtosis model
rolling bearing fault diagnosis method
nonlinear signal
nonsteady signal
imf model
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9086

Internet

https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9086

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