归一Laplacian矩阵有监督最优局部保持映射故障辨识  

Fault Identification Method Based on Normalized Laplacian-based Supervised Optimal Locality Preserving Projection

作　　者：李锋[1] 汤宝平[2] 宋涛[2] 丁行武[1] 

机构地区：[1]四川大学制造科学与工程学院成都610065 [2]重庆大学机械传动国家重点实验室重庆400030

出　　处：《机械工程学报》2013年第13期100-107,共8页Journal of Mechanical Engineering

基　　金：四川大学青年教师科研启动基金(2012SCU11051);重庆市自然科学杰出青年基金(CQ cstc2011jjjq70001);国家自然科学基金(51275546)资助项目

摘　　要：提出基于归一化Laplacian矩阵有监督最优局部保持映射(Normalized Laplacian-based supervised optimal localitypreserving projection,NL-SOLPP)维数化简的故障辨识方法。构造全面表征不同故障特性的时频域特征集,利用NL-SOLPP将高维时频域特征集自动约简为区分度更好的低维特征矢量,并输入到Shannon小波支持向量机中进行故障模式辨识。NL-SOLPP结合流形局部结构和类标签来设计相似加权矩阵,并使输出基矢量统计不相关和相互正交,提高了故障辨识精度。深沟球轴承故障诊断和空间轴承寿命状态辨识实例验证了该方法的有效性。A novel fault diagnosis method based on feature compression with normalized Laplacian-based supervised optimal locality preserving projection （NL-SOLPP） is proposed. The time-frequency domain feature set is first constructed to completely characterize the property of each fault. NL-SOLPP is introduced to automatically compress the high-dimensional time-frequency domain feature sets of training and test samples into the low-dimensional eigenvectors which have better discrimination. The low-dimensional eigenvectors of training and test samples are input into Shannon wavelet support vector machine （SWSVM） to carry out fault identification. NL-SOLPP considers both local information and class labels in designing the similarity weight matrix and requires the output basis vectors to be statistically uncorrelated and orthogonal, therefore, it achieves higher fault identification accuracy. Fault diagnosis example on deep groove ball bearings and life state identification example on one type of space bearing demonstrated the effectivity of proposed method.

关 键 词：时、频域特征集 局部保持映射 维数化简 流形学习 故障辨识 

分 类 号：TH165[机械工程—机械制造及自动化] TN911[电子电信—通信与信息系统]