In order to solve the problem that the stochastic resonance (SR) signal processing method enhances the characteristic signal while enhancing the interference signal, which leads to a lack of desired results, this paper proposed a hybrid resonant sparse (RS) decomposition and tri-stable SR (RSSR) signal method. Firstly, the feasibility and superiority of the RSSR method have been demonstrated by analyzing and comparing the performance of the SR method, the SR method, and the RSSR method in handling single-frequency and multi-frequency signals. Subsequently, the impact of the high-quality factor and the scale transformation parameter on the output performance of the RSSR method was investigated. Additionally, an analysis was conducted on the influence of selecting high-energy sub-bands on the output results in the process of directly applying the RS method to the original signal and in the process of applying the RS method to the output signal of the RSSR method. Finally, a rotor shaft bending fault signal was analyzed using the SR method, the SR method, and the RSSR method, further confirming the superiority of the RSSR method. Experimental data processing results demonstrate that the RSSR method can effectively reduce interference components in the signal, enhance characteristic signals, and improve the signal-to-noise ratio by 59.7% compared to the original signal, by 79.05% compared to the RS method, and by 9.38% compared to the SR method. The results of this study can provide reference and guidance for the extraction of weak characteristic signals.

中文翻译：

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基于混合谐振稀疏分解和三稳态随机共振方法的弱信号增强与提取


为了解决随机谐振 （SR） 信号处理方法在增强干扰信号的同时增强特征信号而缺乏预期结果的问题，本文提出了一种混合谐振稀疏 （RS） 分解和三稳态 SR （RSSR） 信号方法。首先，通过分析和比较 SR 方法、SR 方法和 RSSR 方法在处理单频和多频信号方面的性能，证明了 RSSR 方法的可行性和优越性。随后，研究了优质因子和尺度变换参数对 RSSR 方法输出性能的影响。此外，还分析了在直接将 RS 方法应用于原始信号的过程中以及将 RS 方法应用于 RSSR 方法的输出信号过程中，选择高能量子带对输出结果的影响。最后，采用 SR 法、 SR 法和 RSSR 法分析转子轴弯曲故障信号，进一步证实了 RSSR 法的优越性。实验数据处理结果表明，RSSR 方法能够有效减少信号中的干扰分量，增强特征信号，信噪比原始信号提高 59.7%，比 RS 法提高 79.05%，比 SR 法提高 9.38%。本研究结果可为弱特征信号的提取提供参考和指导。