不同负载下滚动轴承的PSO-SSTCA算法研究  

Research on PSO-SSTCA Algorithm of Rolling Bearings Under Different Loads

作　　者：张泽宇[1,2] 惠记庄 任余[1] 石泽 段雨 ZHANG Zeyu;HUI Jizhuang;REN Yu;SHI Ze;DUAN Yu(Key Laboratory of Road Construction Technology and Equipment,Ministry of Education,Chang'an University,Xi'an 710064,China;Research Center,Tibet Tianlu Co.,Ltd.,Lhasa 850000,China)

机构地区：[1]长安大学道路施工技术与装备教育部重点实验室,西安710064 [2]西藏天路股份有限公司科研中心,拉萨850000

出　　处：《机械科学与技术》2023年第11期1829-1836,共8页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家自然科学基金面上项目(52278390);陕西省秦创原“科学家+工程师”队伍建设项目(2022KXJ-150);陕西省自然科学基金项目(2022JQ-515,2022JM-172);西藏自治区科技计划项目(XZ202101ZR0044G,XZ2019TL-G-02)。

摘　　要：针对不同负载下滚动轴承故障诊断准确率不高和样本稀缺的问题,本文提出了一种基于粒子群优化的半监督迁移学习(PSO-SSTCA)算法。在迁移学习算法的基础上,引入希尔伯特-施密特独立性系数(HSIC)增强迁移学习过程中不同数据标签的依赖性,加入粒子群优化算法自适应寻找多核函数的最优系数,缩小数据集的类内间距,并利用K-近邻算法进行不同负载间滚动轴承的故障诊断。对4种不同负载工况下的滚动轴承振动信号进行分析,结果表明:在单-单、多-单负载工况下,PSO-SSTCA算法的平均准确率分别为85.92%与88%,与重构信号相比分别提高了10.75%与19.42%。该方法有效地为机械设备的状态监测与故障诊断提供了技术支撑。Aiming at the problems of low accuracy of fault diagnosis of rolling bearing under different loads and scarcity of samples,this paper proposes a semi-supervised transfer learning(PSO-SSTCA)algorithm based on particle swarm optimization.On the basis of the transfer learning algorithm,the Hilbert-Schmidt independence coefficient(HSIC)is introduced to enhance the dependence of different data labels in the transfer learning process,and the particle swarm optimization algorithm is added to adaptively find the optimal coefficients of the multi-core function.The intra-class spacing of the data set is reduced,and the Knearest neighbour algorithm is used for fault diagnosis of rolling bearings between different loads.The analysis of rolling bearing vibration signals under four different load conditions shows that the average accuracy of the PSO-SSTCA algorithm is 85.92%and 88%respectively under single-single and multiple-single load conditions,which are lower than the original weight.Compared with the reconstructed signal,it increased by 10.75%and 19.42%respectively.This method effectively provides technical support for the condition monitoring and fault diagnosis of mechanical equipment.

关 键 词：滚动轴承 粒子群算法 迁移学习 特征提取 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化]