分段非对称随机共振系统微弱信号检测

Detection of weak signals in piecewise asymmetric stochastic resonance system

作为一种重要的信号处理方法,随机共振(SR)能够利用噪声能量增强微弱信号,有效降低噪声信号对特征提取的影响。针对分段对称系统模型随机共振幅值增益不够明显及噪声利用率较低等不足,提出一种分段非线性系统模型。该系统参数独立,易于调节,可通过调节参数诱导最佳随机共振。在双稳态模型下,推导了克莱默斯(Kramers)逃逸率和输出信噪比,同时在模型公式仿真和数值仿真两方面与分段对称系统进行对比分析,用于说明该方法的有效性。结果表明该方法能够有效地提取特征频率,具有良好的放大性能和抗噪声能力。最后将系统应用于不同型号的轴承故障检测,并用自适应智能算法最优化系统参数。结果显示,非对称系统的输出幅值分别为对称系统的8倍,3倍和6倍。数据表明,非对称系统能更有效地实现微弱特征检测与早期故障诊断。该研究进一步对系统在实际工程应用提供了理论指导与依据。

As an important signal processing method,stochastic resonance(SR)can use noise energy to enhance weak signals and effectively reduce effects of noise signals on feature extraction.Here,a piecewise nonlinear system model was proposed to solve problems of obviously insufficient stochastic resonance amplitude gain and lower noise utilization rate of piecewise symmetric system model.Parameters of the proposed system were independent and easy to adjust,and its parameters could be adjusted to induce the optimal stochastic resonance.Under the condition of bistable model,Kramers escape rate and output signal-to-noise ratio(SNR)were derived.Meanwhile,model formula simulation and numerical simulation of the proposed model and those of the piecewise symmetric system were analyzed contrastively to illustrate the effectiveness of this method.The results showed that the proposed method can effectively extract feature frequencies,and has good amplification performance and anti-noise ability.Finally,the proposed system was applied in different types of bearing fault detection,the adaptive intelligent algorithm was used to optimize the system parameters,and the results showed that the output amplitude of the asymmetric system is 8 times,3 times and 6 times that of symmetric system,respectively to reveal the asymmetric system being able to more effectively realize weak feature detection and early fault diagnosis;the study further can provide theoretical guidance and basis for the proposed system’s practical engineering applications.