针对滚动轴承复合故障难以分离的问题,课题组提出了一种自适应多尺度形态滤波分离方法。首先,利用具有提取周期性特征的多尺度形态滤波器和峭度特征能量积(kurtosis feature energy product, KF)提取出一种主要的故障特征分量;然后,利...针对滚动轴承复合故障难以分离的问题,课题组提出了一种自适应多尺度形态滤波分离方法。首先,利用具有提取周期性特征的多尺度形态滤波器和峭度特征能量积(kurtosis feature energy product, KF)提取出一种主要的故障特征分量;然后,利用奇异值分解(singular value decomposition, SVD)降噪方法对提取的故障特征进行降噪处理,增强故障特征;最后,对去噪信号进行迭代筛选分离,得到多个故障特征模式分量。通过仿真信号与异步牵引电机实际故障信号对比实验,结果表明:该方法能够分离复合故障特征,并有效提取噪声干扰下的故障特征信息。该方法滤波效果强于传统方法,具有较好的工程应用价值。展开更多
In this paper, the following problems are considered Problem I. Given A ∈Rm×n, D ∈ R n×n. a) Let S1 = {X: X ∈ Rm×n, ||ATX - XTA - D|| = min} find X ∈ S1 such that ||X|| = min; b) Let S2 = {X: X ∈ R...In this paper, the following problems are considered Problem I. Given A ∈Rm×n, D ∈ R n×n. a) Let S1 = {X: X ∈ Rm×n, ||ATX - XTA - D|| = min} find X ∈ S1 such that ||X|| = min; b) Let S2 = {X: X ∈ Rm×n, ATX - XTA = D} find X ∈ S2 such that ||X|| = min. Problem II. Given A ∈ Rm×n,B B∈Rn×p, D ∈Rm×p. Let L1 = {X: X ∈ SRn×n, AXB = D} find X ∈ L1 such that ||X|| = min. Problem III. Given A ∈ Rm×n,B ∈ Rp×q, C ∈ Rm×q, G ∈ Rl×n, H ∈ Rp×t,D ∈ Rl × t. Let L2 = {X: X ∈ Rn×p, AXB = C, GXH = D} find X ∈ L2 such that ||X|| = min. Using singularvalue and canonical correlation decompositions, the necessary and sufficiellt conditions, under which S2, L1 and L2 are nonempty, are studied. The expressions for the solutions of Problems I, II and III are given.展开更多
文摘针对滚动轴承复合故障难以分离的问题,课题组提出了一种自适应多尺度形态滤波分离方法。首先,利用具有提取周期性特征的多尺度形态滤波器和峭度特征能量积(kurtosis feature energy product, KF)提取出一种主要的故障特征分量;然后,利用奇异值分解(singular value decomposition, SVD)降噪方法对提取的故障特征进行降噪处理,增强故障特征;最后,对去噪信号进行迭代筛选分离,得到多个故障特征模式分量。通过仿真信号与异步牵引电机实际故障信号对比实验,结果表明:该方法能够分离复合故障特征,并有效提取噪声干扰下的故障特征信息。该方法滤波效果强于传统方法,具有较好的工程应用价值。
文摘In this paper, the following problems are considered Problem I. Given A ∈Rm×n, D ∈ R n×n. a) Let S1 = {X: X ∈ Rm×n, ||ATX - XTA - D|| = min} find X ∈ S1 such that ||X|| = min; b) Let S2 = {X: X ∈ Rm×n, ATX - XTA = D} find X ∈ S2 such that ||X|| = min. Problem II. Given A ∈ Rm×n,B B∈Rn×p, D ∈Rm×p. Let L1 = {X: X ∈ SRn×n, AXB = D} find X ∈ L1 such that ||X|| = min. Problem III. Given A ∈ Rm×n,B ∈ Rp×q, C ∈ Rm×q, G ∈ Rl×n, H ∈ Rp×t,D ∈ Rl × t. Let L2 = {X: X ∈ Rn×p, AXB = C, GXH = D} find X ∈ L2 such that ||X|| = min. Using singularvalue and canonical correlation decompositions, the necessary and sufficiellt conditions, under which S2, L1 and L2 are nonempty, are studied. The expressions for the solutions of Problems I, II and III are given.
