采用知识的粒子群算法的多频微弱信号自适应随机共振检测方法

Multi-frequency Weak Signal Detection Method Based on Adaptive Stochastic Resonance with Knowledge-based PSO

调参随机共振系统结构参数的选择对该检测方法的性能优劣起着决定性的作用。针对工程应用中对多频微弱信号实时检测的要求,提出以平均输出信噪比为适应度函数,将随机共振系统产生最佳共振效应时势垒与噪声强度大致相等这一特性作为知识,采用基于知识的粒子群算法来并行优化随机共振系统结构参数。与标准粒子群算法相比,该算法能以更快的速度得到最佳的系统结构参数,自适应地实现非线性系统、输入信号和噪声之间的最佳匹配,削弱多频含噪信号中的噪声,提高信号的输出信噪比。仿真试验和水轮机振动信号提取的工程应用均表明,该方法参数寻优效率高,简单易行,在采样点数较少的条件下能最优地检测出淹没在强噪声中的多频微弱信号,可以实现早期故障特征信号的提取。

Suitable structure parameters determine the performance of parameter-induced stochastic resonance detection system.Considering the requirements of real-time detection,the structure parameters are optimized by knowledge-based particle swarm optimization（KPSO）,which takes the mean signal-noise-ratio of the output as the fitness function and the property that stochastic resonance system produces the best resonance effect just when the intensity of noise approximately equals potential barrier as the knowledge.Compared with the PSO,this algorithm can obtain the optimal structure parameters more quickly and adaptively realize optimal matching among the nonlinear system,input signal and noise.Therefore the noise of multi-frequency noisy signal is weakened and signal-noise-ratio of the output is improved.Both simulated experiments and the application of extracting real turbine vibration signals show that the proposed detection method is efficient and feasible.It enables to detect the multi-frequency weak signal submerged in strong noise in case of less sampling points and extract early fault characteristic signal.