A novel stochastic resonance based deep residual network for fault diagnosis of rolling bearing system.

Rolling bearings constitute one of the most vital components in mechanical equipment, monitoring and diagnosing the condition of rolling bearings is essential to ensure safe operation. In actual production, the collected fault signals typically contain noise and cannot be accurately identified. In the paper, stochastic resonance (SR) is introduced into a spiking neural network (SNN) as a feature enhancement method for fault signals with varying noise intensities, combining deep learning with SR to enhance classification accuracy. The output signal-to-noise ratio(SNR) can be enhanced with the SR effect when the noise-affected fault signal input into neurons. Validation of the method is carried out through experiments on the CWRU dataset, achieving classification accuracy of 99.9%. In high-noise environments, with SNR equal to −8 dB, SRDNs achieve over 92% accuracy, exhibiting better robustness and adaptability. • Stochastic resonance is combined with spiking neural networks to improve accuracy. • Diverse neurons are employed in Stochastic Resonance Deep Residual Networks (SRDN). • SRDN exhibits superb robustness and adaptability under different noise conditions. [ABSTRACT FROM AUTHOR]