Tri-stable stochastic resonance coupling system driven by dual-input signals and its application in bearing fault detection

Abstract Stochastic resonance is of great significance for extracting fault signals of bearings. A novel tri-stable stochastic resonance coupling system driven by dual-input signals(DTDTSR) is proposed in this paper, which significantly improve Spectral Amplification(SA) and amplitude of traditional two-dimensional tri-stable stochastic resonance system(TDTSR). Firstly, under the condition of adiabatic approximation theory, the Steady-state Probability Density(SPD), Mean First Pass Time(MFPT) and SA are derived, and the system parameters’ influence on them are analyzed. Then, using SA as the measurement index, numerical simulations are carried out and system parameters are optimized by adaptive genetic algorithm to achieve optimal performance. So DTDTSR, TDTSR and classical tri-stable stochastic resonance system(CTSR) are applied to weak periodic signals detection and compared with each other. The experimental results show that DTDTSR has a large SA and amplitude, which proves that the synergistic effect of coupled system and dual input signal drive can better promote the generation of stochastic resonance. Finally, the three systems and wavelet transform method are applied in two kinds of engineering bearing fault detection, and adaptive genetic algorithm is also used to optimize the system parameters. The experiments reveal are similar to the previous one, proving that DTDTSR is indeed optimal among the three systems. This system is therefore very adaptable and advanced in practical engineering applications.