Weak Fault Feature Extraction Based On Second-order Stochastic Resonance

Vibration analysis is a common method for condition monitoring of mechanical equipment.The vibration signals usually collected from mechanical equipment contain some noise,which makes the characteristic information of useful signals weak and the signal-to-noise ratio(SNR)low,and affects the diagnosis results of bearings.Therefore,how to extract the weak fault characteristic information and improve SNR has become one of the key topics in the field of fault diagnosis.Traditional weak feature extraction methods mostly start with noise reduction,and stochastic resonance(SR)can use noise to enhance the energy of weak signals.In order to solve the problem of the traditional SR method,the second-order tristable SR(STSR)and second-order coupled step-varying SR(SCSSR)method are studied from the SR system mode,based on the basic theory of SR and the existing research methods.Firstly,this paper proposed the STSR based on the classical bistable potential and Woods-Saxon potential.In this method,the output SNR is used as the fitness function of the Seeker Optimization algorithm(SOA),and the adaptive selection of system parameters is realized by combining the second sampling technique,which enhances the characteristics of weak signals and improves the output SNR.The simulation and measured signal analysis results verify the effectiveness of the proposed method.Compared with the traditional SR method,the output signal obtained by this method has more obvious periodicity,higher amplitude spectrum and higher output SNR,so that the weak signal is more easily detected.Secondly,the SCSSR is proposed to solve the problem of many parameters optimization of the STSR.The results of data analysis show that this method can improve the output SNR,reduce the system optimization parameters and improve the calculation efficiency.In order to overcome the shortcomings of output SNR as a measure index of SR,the weighted power spectral kurtosis of the output signal is used to adjust the system parameters to obtain the optimal SR output signal,extract fault information and determine the type of bearing fault.The results of simulation and bearing fault data processing show the reliability of the method.Finally,a bearing fault diagnosis system is developed.The system uses LabVIEW and MATLAB hybrid programming technology to achieve signal acquisition,power spectrum analysis,filter processing,envelope analysis,stochastic resonance analysis and bearing fault characteristic frequency calculation.The reliability of the system is verified by simulation and experimental data,which has certain engineering application value.