带标志点的LTSA算法及其在轴承故障诊断中的应用

针对非监督式流形学习算法面临的增量式学习问题,提出一种带标志点的增量式局部切空间排列算法.该方法在局部切空间排列算法的基础上,利用最小角度回归算法从原始训练样本中选取标志点,以选取的标志点和新增样本建立所有样本的全局坐标矩阵,利用原始样本低维嵌入坐标和全局坐标矩阵对新增样本的低维嵌入坐标进行估计,并采用全局坐标矩阵特征值迭代方法更新所有样本的低维嵌入坐标.滚动轴承4种不同状态振动数据样本的增量式识别结果表明,本方法在实现局部切空间排列算法增量式学习的基础上,保持了对滚动轴承不同状态样本较高的类别可分性测度.

Local tangent space alignment algorithm based on selecting landmark points and its application to Rolling Bearings Fault Diagnosis

The most of unsupervised manifold learning algorithms provide the embedding only for the given training points, with no straightforward extension for incremental points. This paper provides an incremental local tangent space alignment(LTSA) algorithm based on selecting landmark points for the clustering of incremental points. Firstly, the LTSA algorithm is used to construct the global coordinate matrix for the given training points. The landmark points of the given training points are select by the least angle regression(LARS) algorithm. Using the landmark points and given training points construct the global coordinate matrix for all points, and the lower-dimensional embedding coordinate of the incremental points are estimated by the global coordinate matrix and lower-dimensional embedding coordinate of the given training points. Then the lower-dimensional embedding coordinate of all points are updated by the eigenvalue iteration algorithm. Experiments with the proposed method are carried out to identify the four different mode of the vibration signal of rolling bearing. The experiments result demonstrates the incremental LTSA algorithm based on selecting landmark points can preserve the class separability measurement of different sorts on the basis of straightforward incremental learning for out-of-sample.