Open AccessA rolling bearing fault detection method based on compressed sensing and a neural network
Author(s) -
Lu Lu,
Ji You Fei,
Ling Yu,
Yu Yuan
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020313
Subject(s) - compressed sensing , nyquist–shannon sampling theorem , artificial neural network , sampling (signal processing) , computer science , bearing (navigation) , signal (programming language) , nonlinear system , set (abstract data type) , fault (geology) , compression (physics) , transmission (telecommunications) , fault detection and isolation , data set , data compression , pattern recognition (psychology) , real time computing , artificial intelligence , computer vision , telecommunications , geology , physics , materials science , filter (signal processing) , quantum mechanics , seismology , actuator , composite material , programming language
The high sampling frequency of traditional Nyquist sampling theory not only puts greater requirements on the sampling equipment, but also generates a large amount of data, which increases the difficulty of information transmission and storage. To this end, this paper proposes a rolling bearing fault signal detection method based on compressed sensing combined with a neural network. Based on the theory of compressed sensing, the observations obtained from compression sampling are divided into two sets of data. Given the one set of data, the predictive ability of the nonlinear time series through the neural network can predict the second set of observed values. The predicted observations are used to reconstruct the signal, thereby reducing the amount of data to be stored and transmitted and realizing secondary compression of the signal.