Fault diagnosis approach of rolling bearing based on NA-MEMD and FRCMAC

Fault diagnosis approach of rolling bearing based on NA-MEMD and FRCMAC

This paper proposed a new method of fault diagnosis based on Noise Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) and Fuzzy Recurrent Cerebellar Model Articulation Controller (FRCMAC) Neural Networks. Aiming at the problem that during the use of the NA-MEMD method, the white noise ampl...

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Bibliographic Details
Main Authors: Sheng Liu, Yue Sun, Lanyong Zhang
Format: Article
Language:English
Published: Wiley 2018-10-01
Series:The Journal of Engineering
Subjects:
genetic algorithms
acoustic signal processing
cerebellar model arithmetic computers
rolling bearings
neurocontrollers
mechanical engineering computing
white noise
vibrations
machine bearings
fault diagnosis
fault diagnosis approach
noise-assisted multivariate empirical mode decomposition
fuzzy recurrent cerebellar model articulation controller neural networks
artificial experience
genetic algorithm
auxiliary white noise parameters
FRCMAC structure
fuzzy processing
input space
association degree
Gaussian function
association unit
autoregressive unit
dynamic mapping
traditional CMAC structure
GA-NA-MEMD method
intrinsic mode functions
IMFs
fault feature vectors
FRCMAC neural network
neural network structure suitable
bearing fault diagnosis
Bearing Data Center
fault diagnosis method
diagnosis time
precision
fault diagnosis results
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8991

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https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8991

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