Weak fault diagnosis of rolling bearing based on improved stochastic resonance

Document Type

Article

Publication Date

1-1-2020

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.

Identifier

85090888768 (Scopus)

Publication Title

Computers Materials and Continua

External Full Text Location

https://doi.org/10.32604/CMC.2020.06363

e-ISSN

15462226

ISSN

15462218

First Page

571

Last Page

587

Issue

1

Volume

64

Grant

51405241

Fund Ref

National Natural Science Foundation of China

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