摘要
针对电机轴承易发生故障,传统故障诊断方法具有耗时长、诊断精度低、调节参数多等问题,提出一种改进麻雀搜索算法ISSA优化支持向量机SVM的轴承故障诊断方法。该分类算法在传统麻雀寻优算法中引入改进Tent混沌映射、鸡群算法随机跟随策略、自适应t分布与动态选择策略,首先采用CEEMDAN能量熵对振动信号进行分解,选取与原信号相关性最大的5个IMF分量的能量熵值作为特征向量,然后输入到ISSA-SVM分类器中进行轴承故障诊断。分别与PSO-SVM、GWO-SVM、SSA-SVM分类模型进行实验对比,结果表明ISSA-SVM诊断模型的诊断精度最高可达到100%。
Aiming at the problems of motor bearings being prone to failure,the traditional fault diagnosis method has long time,low diagnostic accuracy and many adjustment parameters,and this paper proposes a bearing fault diagnosis method for support vector machine SVM optimized by improving sparrow algorithm ISSA.The classification algorithm introduces improved Tent chaos mapping,flock algorithm random following strategy,adaptive t distribution and dynamic selection strategy in the traditional sparrow optimization algorithm,and first uses CEEMDANenergy entropy to decompose the vibration signal,selects the energy entropy values of the five IMF components with the greatest correlation with the original signal as the eigenvector,and then inputs it to the ISSA-SVM classifier for bearing fault diagnosis.Experimental comparison with PSO-SVM、GWO-SVM and SSA-SVM classification models shows that the diagnostic accuracy of the ISSA-SVM diagnostic model can reach up to 100%.
作者
杨旭
张涛
李玉梅
刘洪
Yang Xu;Zhang Tao;Li Yumei;Liu Hong(Beijing Key Laboratory of High Dynamic Navigation Technology,Beijing Information Science&Technology University,Beijing 100101,China;Key Laboratory of Modern Measurement&Control Technology,Ministry of Education,Beijing Information Science&Technology University,Beijing 100101,China)
出处
《电子测量技术》
北大核心
2023年第15期186-192,共7页
Electronic Measurement Technology
基金
北京信息科技大学重点研究培育项目(2121YJPY220)
北京市教委一般项目(KM202111232004)
中国石油天然气集团有限公司-中国石油大学(北京)战略合作科技专项(ZLZX2020-03)资助
关键词
支持向量机
滚动轴承
麻雀搜索算法
相关系数
support vector machine
rolling bearing
sparrow search algorithm
correlation coefficient
作者简介
杨旭,硕士,主要研究方向为电机轴承故障检测方法研究、电机在线监测系统设计。E-mail:13673283583@163.com
