PSO-COT与EEMD的变转速滚动轴承故障特征提取  

Fault Feature Extraction of Variable Speed Rolling Bearing Based on PSO-COT and EEMD

作　　者：高大涌 付志鹏 苑宗昊 白雪飞 Gao Dayong;Fu Zhipeng;Yuan Zonghao;Bai Xuefei(School of Electrical and Electronic Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;School of Traffic and Transportation,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)

机构地区：[1]石家庄铁道大学电气与电子工程学院,河北石家庄050043 [2]石家庄铁道大学交通运输学院,河北石家庄050043

出　　处：《石家庄铁道大学学报（自然科学版）》2021年第4期53-58,共6页Journal of Shijiazhuang Tiedao University(Natural Science Edition)

基　　金：国家自然科学基金重大项目(11790282);国家自然科学基金面上项目(12072207);石家庄铁道大学研究生创新资助项目(YC2020070)。

摘　　要：针对滚动轴承在变转速工况下微弱故障特征难以提取的问题,提出了PSO-COT与EEMD的变转速滚动轴承故障特征提取方法。首先,通过粒子群优化算法(particle swarm optimization,PSO)寻找最优过采样率,对采集到的滚动轴承振动信号进行过采样;然后,利用计算阶比跟踪(computed order tracking,COT)将过采样后的时域信号转变成角域的平稳信号;最后,通过集合经验模态分解(ensemble empirical mode decomposition,EEMD)去噪,经过阶次谱分析滚动轴承故障特征阶次。实验表明该方法具有较好的故障特征提取精度,可以有效提取变转速工况下滚动轴承故障特征信息。Aiming at the problem that it is difficult to extract weak fault features of rolling bearing under variable speed conditions,a fault feature extraction method of variable speed rolling bearing based on PSO-COT and EEMD was proposed.Firstly,particle swarm optimization(PSO)was used to find the optimal oversampling rate and oversampling the collected rolling bearing vibration signal.Then,the over-sampled time-domain signal was transformed into a stationary signal in the angular domain by computed order tracking(COT).Finally,the noise was removed by ensemble empirical mode decomposition(EEMD),and the fault characteristic order of rolling bearing was analyzed by order spectrum.The experiments show that this method has good fault feature extraction accuracy and can effectively extract the fault feature information of rolling bearing under variable speed conditions.

关 键 词：粒子群 计算阶比跟踪 EEMD 滚动轴承 

分 类 号：TH133.33[机械工程—机械制造及自动化]