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中文摘要：
在变步长随机共振原理的基础上，提出一种自适应随机共振微弱信号检测技术。该技术以粒子群算法（particle swarm optimization，PSO）作为优化算法，利用随机共振系统输出信号的奇异熵合理构造适应度函数，同时外加可控信号，以更好地激励出随机共振现象。该技术实现了随机共振系统结构参数、外加信号强度以及算法数值计算步长的自适应选取，能够最优地检测出大参数条件下信号中的微弱周期成分。仿真信号和滚动轴承故障信号的应用表明，该方法在短数据条件下可以取得较好的检测结果，具有良好的工程应用前景。
英文摘要：
A novel adaptive stochastic resonance （SR） system with application in weak signal detection is proposed based on the step-changed SR. This method uses particle swarm optimization （PSO） as the optimization algorithm. A reasonable fitness function including singular entropy of SR output signal is constructed and a controllable signal is added in this system to produce a better result. This method implements the automatic selection of the system parameters, the strength of the additional signal and its calculation step, and it could be adapted to weak signal detection in large parameters. Tests in simulation data and vibration signal measured on defective bearings show that the proposed method could identify the characteristic signal effectively in case of small number of sample points and it has a promising prospect in the application of mechanical engineering.
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