Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance

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.