Research On Rolling Bearing Fault Diagnosis Based On Parameter Optimization Variational Mode Decomposition

With the continuous development of the domestic economy,science and technology,people’s demand for productivity has become greater and greater.The traditional way of creating productivity by hand has changed greatly.Many mechanical equipment have appeared in the industrial field for social development.Rolling bearing is one of the core components of mechanical equipment,its failure or not plays an important role in the safety of the whole equipment operation.It has become a prominent research topic to judge whether the rolling bearing has fault and what the type of the fault.This paper takes rolling bearings as the research object for corresponding fault diagnosis research.The main contents of the full text are as follows:(1)For the variational mode decomposition(VMD)algorithm in adaptive analysis,firstly,the parameters are set by the traditional method of determining one and finding two,and then the influence of different parameter combinations on signal decomposition is analyzed.Considering the limitations and subjective factors of traditional methods,use quantum particle swarm optimization algorithm to optimize the parameter combination as the research cornerstone of the proposed fault diagnosis method.(2)Considering the fault feature information of rolling bearing vibration signal is difficult to extract due to the interference of noise and resonance,this paper propose a rolling bearing fault diagnosis method based on parameter optimization variational mode decomposition and fast spectral kurtosis.The specific step of this idea is to demodulate the processed signal through frequency domain analysis method to form envelope spectrum,and then compare the information in envelope spectrum with fault characteristic frequency to get fault diagnosis conclusion.(3)Because the construction of eigenvectors in the intelligent diagnosis method of rolling bearing affects the final diagnosis result,this paper proposes a rolling bearing fault diagnosis method based on parameter optimization variational mode decomposition and permutation entropy based on kurtosis criterion.The key point is to propose a method based on kurtosis criterion to select modal components and single-scale permutation entropy to construct feature vectors and use the example signals to verify the performance of the proposed method.(4)Because the information of vibration signal can not be extracted to the greatest extent when using single scale information entropy to construct feature vector,a rolling bearing fault diagnosis method based on parameter optimization variational mode decomposition and optimal multi-scale permutation entropy is proposed.The key point is to put forward the concept of "rotating mechanism" and the optimal multi-scale permutation entropy.Finally,we find the accuracy of this method is 100% by the verification of the example signal.Through the research on the parameter setting of the variational mode decomposition algorithm and the fault diagnosis method of rolling bearing,it can provide the corresponding theoretical reference for the practical application of the future engineering,which has the great significance of “predecessors planting trees,descendants enjoy the cool”.