Stochastic resonance in cascaded monostable systems with double feedback and its application in rolling bearing fault feature extraction

Abstract

Stochastic resonance (SR) has been widely concerned and studied in the field of mechanical fault detection due to the capability of weak signal detection. However, research focused on how to improve the performance of SR system and enable it to process large-parameter signals in engineering practice better. Therefore, from the perspective of feedback control and multi-system synergy, this paper presents a double-feedback cascaded monostable SR (CMSR) system and analyzes the SR phenomenon in the system by using the signal-to-noise ratio as the measurement index. It investigates specifically influence of the coefficients on the system performance. Introducing feedback control and serial structure to a monostable system, consisting of a symmetric quartic potential function, not only alters the response characteristics of the monostable system but also significantly improve the system performance. Compared with the classical bistable SR system with low-pass filtering characteristics, the proposed system can exhibit low-pass filtering or band-pass filtering behavior through different combinations of feedback coefficients, which can better match the frequency characteristic of target signal. Finally, the proposed double-feedback CMSR system is used for extraction of the weak signal features in rolling bearing fault detection, and its superiority and feasibility are verified by comparison with other SR systems.