Weak signal detection based on stochastic resonance combining with genetic algorithm

In order to detect a weak periodic signal under the condition of intensive noise, the weak signal including strong background noise and adscititious multiplicative noise were used as input of a monostable stochastic resonance (SR) system. The adscititious noise intensity and the system parameter were adjusted adaptively with genetic algorithm by examining the SR effect on output signal-to-noise ratio (SNR). An improved numerical solution for a monostable SR model based on a fourth order Runge-Kutta algorithm was presented to enhance the SR effect. The simulation results show that the weak signal in an intensive noisy background could be successfully extracted. What is more, the output SNR was increased more than 30 dB comparing with the input SNR. It can be seen that the proposed method was superior to the traditional spectra analysis and envelope demodulation methods in detecting the weak periodic signal. Such detection approach indicates a promising prospect for mechanical fault monitoring and diagnosis.