Two-Dimensional Tri-stable Stochastic Resonance system and its application in bearing fault detection

Gang Zhang, Xiaoman Liu and Tianqi Zhang

Physica A: Statistical Mechanics and its Applications, 2022, vol. 592, issue C

Abstract: A Two-Dimensional Tri-stable Stochastic Resonance (TDTSR) system with adjustable parameters is proposed to address the problem of difficult parameter tuning in one-dimensional stochastic resonance systems. Firstly, under the limitation of adiabatic approximation theory, the Steady-state Probability Density (SPD) and output Signal-to-Noise Ratio (SNR) of the system are derived, and the effects of system parameters (a, b, c, r, k) on them are analyzed. The results prove that particles are more capable of transiting between potential wells to obtain better resonance effect when the system is coupled. Then, TDTSR system and the One-Dimensional Tri-stable Stochastic Resonance (ODTSR) system are analyzed and simulated numerically respectively, which show that the Mean Signal-to-Noise Ratio Gain (MSNRG) of TDTSR system is greater than that of ODTSR system. Finally, the two systems are applied in bearing faults detection, and the system parameters are optimized by genetic algorithm. The experimental results indicate that TDTSR system is excellent than ODTSR system, which provides a great theoretical significance and practical value for engineering applications.

Keywords: Fault detection; Stochastic resonance; TDTSR; MSNRG; SNR (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:592:y:2022:i:c:s0378437122000012

DOI: 10.1016/j.physa.2022.126855

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