Fault Diagnosis Method of Rolling Bearing Based on ESGMD-CC and AFSA-ELM  

作　　者：Jiajie He Fuzheng Liu Xiangyi Geng Xifeng Liang Faye Zhang Mingshun Jiang 

机构地区：[1]School of Traffic&Transportation Engineering,Central South University,Changsha,410075,China [2]Project Management Department,CRRC Advanced Composites Co.,Ltd.,Qingdao,266108,China [3]School of Control Science and Engineering,Shandong University,Jinan,250061,China

出　　处：《Structural Durability & Health Monitoring》2024年第1期37-54,共18页结构耐久性与健康监测（英文）

基　　金：supported by National Key Research and Development Project (2020YFE0204900);National Natural Science Foundation of China (Grant Numbers 62073193,61873333);Key Research and Development Plan of Shandong Province (Grant Numbers 2019TSLH0301,2021CXGC010204).

摘　　要：Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods,making it challenging to ensure the fault diagnosis accuracy and reliability.A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator(ESGMD-CC)and artificial fish swarm algorithm(AFSA)optimized extreme learning machine(ELM)is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis.Firstly,SGMD decomposes the raw vibration signal into multiple Symplectic geometry components(SGCs).Secondly,the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components.Additionally,the calculus operator is performed to strengthen weak fault features and make them easier to extract,and the singular value decomposition(SVD)weighted by power spectrum entropy(PSE)can be utilized as the sample feature representation.Finally,AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification.The superior performance of the proposed method has been validated by various experiments.

关 键 词：Symplectic geometry mode decomposition calculus operator cosine difference limitation fault diagnosis AFSAELM model 

分 类 号：TH133.33[机械工程—机械制造及自动化]