摘要
为准确识别滚动轴承当前故障状态,提出一种集合经验模态分解(EEMD)、特征选择与t-分布邻域嵌入(t-SNE)的诊断方法。采用EEMD分解故障信号获得若干本征模态函数(IMF),采用峭度准则筛选有效IMF分量并重构;求出重构信号的高维时、频域特征矩阵并对其归一化,采用t-SNE算法获得对故障状态更敏感的低维特征矩阵;将特征矩阵输入粒子群优化的最小二乘支持向量机(LSSVM)中,实现轴承的故障识别与诊断。采用实验分析并对比几种典型的降维法,证明了t-SNE的优越性,所提方法可以实现故障状态的100%识别,验证了该方法的有效性。
To identify the fault state of rolling bearing accurately,a diagnosis method is proposed which is based on ensemble empirical mode decomposition(EEMD)and t-distribution stochastic neighborhood embedding(t-SNE).Firstly,EEMD is used to decompose vibration fault signal to obtain several intrinsic mode functions(IMF),and the kurtosis criterion is used to select effective IMF components and reconstruct them.Secondly,the high dimensional time-frequency characteristic matrix of the reconstructed signal is obtained and normalized,and a low dimensional characteristic matrix which is more sensitive to fault states is obtained by t-SNE algorithm.Finally,characteristic matrix is input into the least squares support vector machine(LSSVM)classifier which is optimized by particle swarm optimization algorithm to realize the fault state recognition and diagnosis of bearing.In the case analysis,the bearing state data of Western Reserve University and MFPT is used,results show that the advantages of t-SNE by comparing several typical dimension reduction methods and the proposed method can realize 100%fault recognition.
作者
殷秀丽
谢丽蓉
杨欢
段智峰
YIN Xiuli;XIE Lirong;YANG Huan;DUAN Zhifeng(Engineering Research Center for Renewable Energy Power Generation and Grid Technology of Ministry of Education,Xinjiang University,Urumqi 830047,China;State Key Laboratory of Power System and Generation Equipment,Tsinghua University,Beijing 100084,China)
出处
《机械科学与技术》
CSCD
北大核心
2023年第11期1784-1793,共10页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(62163034)
新疆维吾尔自治区高校科研计划自然科学重点项目(XJDU2020I004)
新疆维吾尔自治区区域协同创新专项(科技援疆计划)(2018E02072)。
关键词
故障诊断
集合经验模态分解
特征选择
t-分布邻域嵌入
fault diagnosis
ensemble empirical mode decomposition(EEMD)
time-frequency domain characteristics
t-distributed stochastic neighbor embedding(t-SNE)
