Extracting non-stationary signal under strong noise background: Time-varying system analysis

The extraction of non-stationary feature information under strong noise background is a difficult problem. In this paper, a novel general time-varying scale transformation aperiodic stochastic resonance is proposed to extract and enhance the weak non-stationary signal under strong noise background. The theoretical framework of a parameters time-varying Duffing system is built for aperiodic stochastic resonance. By studying the resonance region migration when scale coefficient takes different values, an optimal scale transformation is achieved. Also, the time-varying system is optimized with cross-correlation coefficient as the index. Compared with the existing methods, the proposed method can be applied to stronger noise background and has stronger noise robustness. When under the same noise background, the proposed method can provide output with higher signal-to-noise ratio and higher cross-correlation coefficient. Finally, experimental analysis of faulty bearing vibration signal verifies the high accuracy, which indicates a good signal extraction and enhancement ability of the proposed method.