面向状态监测的改进主元分析方法.doc

文档介绍：面向状态监测的改进主元分析方法*
韦洁1,2 张和生1,2 贾利民1
(1. 轨道交通控制与安全国家重点实验室, 北京 100044; 2. 北京交通大学电气工程学院, 北京 100044)
摘要: 为满足牵引电动机状态监测中多维海量数据处理的需求, 给出了一种基于改进主元分析的状态监测方法。该方法以均值化代替标准化对传统主元分析进行改进, 在保留原有数据信息特征的基础上降低指标维数, 消除变量关联, 建立主元模型, 利用SPE统计量和T2统计量判断电机运行是否发生异常。实验结果表明: 基于改进主元分析的状态监测方法能够建立准确的状态监测统计模型并快速检测出电机异常情况, 该方法在电机状态监测中是有效可行的。
关键词: 状态监测;主元分析;牵引电动机;数据预处理
中图分类号: 文献标识码: A 国家标准学科分类代码:
State monitoring approach based on improved ponent analysis
Wei Jie1,2 Zhang Hesheng 1,2 Jia Limin1
(1. State Key Laboratory of Rail Traffic Control and Safety, Beijing 100044, China;
2. School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)
Abstract: In order to meet the demands of multidimensional and mass data in traction motor state monitoring, a new kind of ponent Analysis (PCA) approach is proposed. This method, whose data preprocessing is improved, is an effective way which can not only reduce the dimension of motor index and eliminate correlation between process variables, but also reserve enough information of original data characteristics needed for modeling. Based on PCA model, a state monitoring experiment is carried out on a traction motor with SPE and T2 statistics. The experiment results validate that the approach can build an accurate monitoring model and detect abnormal state of motor effectively.
Keywords: state monitoring; PCA; traction mot