基于自适应LTSA算法的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on adaptive LTSA algorithm

针对故障特征集维数高以及冗余的问题,提出一种自适应邻域选择的改进局部切空间排列维数约简方法.通过考虑流形的采样密度、局部弯曲度和局部切空间近似偏离角度,自适应构建样本邻域,以保证局部线性度,能提高算法鲁棒性.为提高故障诊断准确率,提出改进Fisher准则的特征评价方法,首先对原始特征集进行特征选择,优选出能表征类间散度大、类内散度小和低冗余的故障特征,然后采用改进的局部切空间排列算法进行特征融合,得到低维的敏感特征子集,并输入到k最近邻分类器进行故障识别.用滚动轴承不同部位、不同故障程度的实验数据验证了该方法的有效性.

Improved local tangent space alignment（ILTSA）method with adaptive neighborhood selection was presented,aiming at solving the problem of over-high dimensions and redundancy in the mixed fault feature set.As traditional neighborhood selection method was not applicable to the varied curvature and non-uniformly sampled manifold,to keep the local linearity by considering the sample density,the local curvature and the deflection angle of local tangent space,method of selecting the neighborhood adaptively was proposed to improve the robustness of the algorithm.An improved Fisher criterion method of feature selection was also proposed to improve the accuracy of fault diagnosis.Firstly the low redundant features were selected to make the high dispersion between classes and low dispersion within a class.Then the sensitive features were compressed to reduce dimensions with the ILTSA method.Finally,the feature subset was fed into the knearest neighbor classification（KNNC）to identify the fault.The test on different fault position and severities of rolling bearing verified the validity of the proposed method.