| Weak signal detection which belongs to signal detection technology is an emerging field,Weak signal detection technology can be applied to all aspects of national defense,life science,biomedical engineering,production and other aspects of life.Stochastic resonance is a nonlinear phenomenon to enhance the weak signal transmission using noise.Compared with the linear methods,stochastic resonance can transform the part of the noise energy into signal energy and highlight the signal to be detected.The signal with Lower signal-to-noise ratio(SNR)can be detected using stochastic resonance in the short data set condition.Stochastic resonance also is fast and suitable for real-time applications.Therefore,this new signal processing method has been widespread concerned.According to different noisy signal,Adaptive stochastic resonance system can automatically adjust the structural parameters of nonlinear system and extract the weak feature signal drowned in the noise.therefore,adaptive stochastic resonance has important practical value in practical engineering applications.In this paper,the weak signal detection method based on adaptive stochastic resonance is studied.The main contents of the paper include the followings:(1)The research background and significance of paper are introduced.The origin and development of stochastic resonance are reviewed.The research status of stochastic resonance phenomenon at home and abroad is summarized.The research direction and contents of subject are described.The research status of adaptive describes at home and abroad is discussed.The problems to be solved are presented.The contents of the paper are given.(2)The source of nonlinear Langevin Equation is introduced from the moving regulation of Brown particles in liquid.The dynamics model of bistable system is introduced,and mechanism of detecting weak periodic signal under bistable system is analyzed.The principle of twice sampling stochastic resonance is described.(3)The structure parameters optimizion of bistable stochastic resonance system incentived by noisy single frequency weak signal is regarded as research object.The evaluation indexeswhich can measure performance of adaptive optimization algorithm are proposed.The optimizion performance of several intelligent algorithms which include genetic algorithm,particle swarm algorithm,and ant colony algorithm are studied and compared under the structure parameters optimization of stochastic resonance system.(4)Aiming at multi-frequency weak signal(including multi-frequency small parameter,multi-frequency large parameter,broadband multi-frequency large parameters)detection,the mean signal noise ratio(MSNR)of the output is proposed and taken as the multi-frequency weak signal measure index of stochastic resonance effect.And the multi-frequency weak signal detection method based on adaptive stochastic resonance with knowledge-based particle swarm optimization(KPSO)is proposed.Simulation results show that multi-frequency weak signal can be effectively detected,compared to the standard particle swarm algorithm,KPSO improves the performance and efficiency of parameter optimization.The application of extracting real turbine vibration signals shows 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.The effectiveness of this method was further verified.(5)Aiming at weak shock signal detection,the weak shock signal detection method based on adaptive stochastic resonance with knowledge-based particle swarm optimization(KPSO)is proposed.Simulation results show that weak shock signal can be effectively detected,compared to the standard particle swarm algorithm,KPSO also improves the performance and efficiency of parameter optimization.(6)Aiming at weak digital pulse signal detection,the weak digital pulse signal detection method based on adaptive stochastic resonance with knowledge-based particle swarm optimization(KPSO)is proposed.Simulation results show that weak digital pulse signal can be effectively detected,compared to the standard particle swarm algorithm,KPSO improves the performance and efficiency of parameter optimization. |
