Weak Fault Diagnosis of Rolling Bearing Based on Iterative SGMD and Improved MOMEDA
A fault diagnosis method based on iterative symplectic geometry mode decomposition ( ISGMD) and improved multipoint optimal minimum entropy deconvolution adjustment ( IMOMEDA) was proposed to solve the problem of weak fault characteristics of rolling bearings under strong background noise.Firstly,the fault signal is decomposed by ISGMD and the optimal component is selected based on the comprehen-sive index.Secondly,the fault period of MOMEDA is determined according to multipoint kurtosis spec-trum,the filter length is adaptive optimized by egret swarm optimization algorithm ( ESOA),and the opti-mal component is deconvolved by IMOMEDA.Finally,the envelope spectrum of the deconvolution signal is analyzed,and the fault characteristic frequency is extracted to complete the fault diagnosis.Simulation and experimental results show that the proposed method can effectively extract the weak fault characteristic information of rolling bearings under strong background noise.
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维普
