Application of a vibration resonance-assisted enhanced feedforward cascaded stochastic resonance system in bearing diagnostics

Gang Zhang, Wenhao Liu, Qing Xiong and Luming Lei

Chaos, Solitons & Fractals, 2024, vol. 188, issue C

Abstract: Extracting fault features from strong background noise is a key challenge in bearing diagnostics. Stochastic resonance (SR) has been widely applied in bearing diagnostics by utilizing noise to enhance useful signals. This paper proposes a feedforward cascaded system based on vibration resonance (VR) to assist in enhancing SR from the perspective of multi-system synergy, aiming to improve the performance of the SR system. Firstly, a matching steady-state potential function is designed to effectively alleviate the output saturation phenomenon. Then, by introducing a feedback term and VR, a feedforward cascaded system with band-pass characteristics is constructed, significantly enhancing the ability to suppress low-frequency interference. Finally, the proposed system is analyzed in the detection of simulated signals and actual bearing fault signals from the German PADERBORN dataset. The results demonstrate that the system outperforms two other SR systems and the Fast-Kurtogram method (FK), validating its performance and practicality.

Keywords: Stochastic resonance; Vibration resonance; Feedback system; Cascaded system; Bearing diagnosis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:188:y:2024:i:c:s0960077924011056

DOI: 10.1016/j.chaos.2024.115553

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