基于邻域自适应局部保持投影的轴承故障诊断模型

为了有效利用振动信号进行故障诊断，提出了一种基于邻域自适应局部保持投影的轴承故障诊断模型。首先，利用EMD将轴承振动信号分解为若干个平稳的固有模态函数（IMF），对IMF分量建立自回归（AR）模型，构建原始特征子集。然后，利用邻域自适应局部保持投影算法对原始特征子集进行降维处理，获得原始特征子集的低维特征向量和投影矩阵。以低维特征向量为输入，以最小二乘支持向量机（LS-SVM）为分类器，通过研究故障识别率和低维特征空间维数的关系确定最优降维维数和对应的最优投影矩阵。最后，根据最优降维维数完成降维处理过程，得到低维特征向量，输入LS-SVM分类器，识别轴承的工作状态和故障类型。实验结果表明，该模型提高了轴承故障诊断的精度。

Bearing fault diagnosis model based on neighborhood adaptive locality preserving projections

In order to diagnose fault effectively by using vibration signal, a bearing fault diagnosis model based on neighborhood adaptive locality preserving projections was proposed. Firstly, the bearing vibration signal was decomposed into several smooth intrinsic mode functions (IMFs) by EMD and the auto-regressive (AR) model of IMF was established to construct original characteristic subset. Then, the algorithm of neighborhood adaptive locality preserving projections was used to reduce the dimension of original characteristic subset to gain low-dimension eigenvectors and projection matrix. The best reduced dimension and the best corresponding projection matrix could be determined by studying the relationship between fault recognition rate and dimension of the low-dimension eigenspace with the low-dimension eigenvectors as inputs and least square support vector machine(LS-SVM)as classifier. At last Low-dimension eigenvectors converted from original characteristic subset based on the best reduced dimension were put into LS-SVM for recognizing the conditions and fault states of bearing. Results of the test indicated that the proposed model diagnosed bearing fault with high accuracy.