@Article{cmc.2020.06363,
AUTHOR = {Xiaoping Zhao, Yifei Wang, Yonghong Zhang, Jiaxin Wu, Yunqing Shi},
TITLE = {Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {571--587},
URL = {http://www.techscience.com/cmc/v64n1/39160},
ISSN = {1546-2226},
ABSTRACT = {Stochastic resonance can use noise to enhance weak signals, effectively 
reducing the effect of noise signals on feature extraction. In order to improve the early fault 
recognition rate of rolling bearings, and to overcome the shortcomings of lack of 
interaction in the selection of SR (Stochastic Resonance) method parameters and the lack 
of validation of the extracted features, an adaptive genetic random resonance early fault 
diagnosis method for rolling bearings was proposed. compared with the existing methods, 
the AGSR (Adaptive Genetic Stochastic Resonance) method uses genetic algorithms to 
optimize the system parameters, and further optimizes the parameters while considering 
the interaction between the parameters. This method can effectively extract the weak fault 
features of the bearing. In order to verify the effect of feature extraction, the feature signal 
extracted by AGSR method was input into the Fully connected neural network for fault 
diagnosis. the practicality of the algorithm is verified by simulation data and rolling bearing 
experimental data. the results show that the proposed method can effectively detect the 
early weak features of rolling bearings, and the fault diagnosis effect is better than the 
existing methods.},
DOI = {10.32604/cmc.2020.06363}
}