Research On Vibration Feature Extraction And Fault Diagnosis Of Rolling Bearing

As a key supporting component,the rolling bearing is widely used in rotating machinery and the fault diagnos of rolling bearing has always been a research hotspot to improve the economic benefit and ensure the operation safety.In the working process,the failure of rolling bearing is inevitable due to the change of speed,load and lubrication state.Vibration signal reflects rich mechanical operation information,through which fault location and type identification of rolling bearing can be realized.However,in complex fault states,the vibration signal components of rolling bearing are complex,and the fault features are difficult to extract accurately,which brings great challenges for fault diagnosis.Therefore,this paper studies the fault feature extraction and diagnosis method of rolling bearing based on vibration signal.Under single and composite fault modes,based on wavelet analysis and neural network,this paper studies the precision fault diagnosis method and optimization scheme of rolling bearing under different fault modes.The main research contents include:(1)Study of vibration signal feature extraction.Under the single fault and composite fault modes of rolling bearing,two feature extraction methods are proposed:for the single fault of rolling bearing,wavelet analysis is proposed to denoise the vibration signal,and then the energy features are extracted through wavelet packet decomposition,and the basis functions and decomposition levels of the two are optimized.For the composite fault of rolling bearing,wavelet packet combined with AR(Auto Regressive)spectrum is proposed to extract the energy entropy feature of vibration signal,and the wavelet packet basis function and decomposition level are optimized.The experimental results show that the two methods can accurately extract the features of the corresponding fault modes.(2)Fault diagnosis model study.The fault diagnosis method of rolling bearing is studied by using the vibration acceleration signal of single shell.For single fault of rolling bearing,an ANVTPSO-BPNN fault diagnosis model based on adaptive no velocity term particle swarm optimization(ANVTPSO)is proposed to optimize the threshold and weight parameters of BP neural network(BPNN).In the composite fault mode of rolling bearing,a SOM-BPNN network model combining self-organizing maps(SOM)and BPNN is proposed.ANVTPSO is used to optimize the threshold and weight parameters of SOM-BPNN,and ANVTPSO-SOM-BPNN fault diagnosis model is constructed.The performance of ANVTPSO-BPNN and ANVTPSO-SOM-BPNN models is verified by experiments.The results show that under the single and composite fault mode of rolling bearing,the diagnostic accuracy of both models was significantly improved compared with BPNN.(3)Multi-vibration information fusion diagnosis study.Compared with a single fault,the situation of rolling bearing rotor composite fault is more complex.Using a single sensor signal is easy to cause the difficulty of fault feature extraction and accurate fault diagnosis.Two ways of dual acceleration signal and combined acceleration displacement signal are proposed for different mode composite fault diagnosis.ANVTPSO-SOM-BPNN method is used for fault diagnosis of each single signal,and then the decision-making layer fusion of diagnosis results is carried out through Dempster-Shafer(D-S)evidence theory,so as to realize the reliable diagnosis of rolling bearing rotor composite fault.The effect of the rolling bearing rotor composite fault diagnosis method based on multi vibration information fusion is verified by experiments.The results show that the diagnosis accuracy after fusion is 98.28%in the acceleration combined displacement signal mode,which is 6.86%higher than that in the dual acceleration signal mode.