文献类型：期刊文献

中文题名：基于多变量频域分解的动态时频过程监控方法研究

英文题名：Research on Dynamic Time-frequency Process Monitoring Based on Multivariate Spectral Decomposition

作者：夏春明[1];刘长利[1];郑建荣[1]

机构：[1]华东理工大学机械工程系,上海200237

年份：2007

卷号：36

期号：5

起止页码：568

中文期刊名：信息与控制

外文期刊名：Information and Control

收录：CSTPCD;;Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金：教育部留学回国人员科研启动基金资助项目

语种：中文

中文关键词：过程监控;频域独立成分分析;时频分析;故障检测

外文关键词：process monitoring;spectral independent component analysis（spectral ICA）;time-frequency analysis;fault detection

摘要：提出一种基于多变量频域分解的新型动态时频监控方法.结合已有的频域独立成分分析方法以及带约束的非负分解处理,引入时间滑动窗口,在短时窗内动态提取多重主导功率频谱.提出了多种趋势图,以及反映过程变量对主导频谱贡献程度的显著度指标图.该方法能有效地监控过程系统中主导成分的频率、能量的变化趋势以及过程变量的贡献度,适合于非稳态过程监控以及故障检测与定位.仿真实验表明了该方法是可行的.
　A novel dynamic time-frequency process monitoring method based on multivariate spectral decomposition is proposed.Based on the existing spectral independent component analysis（spectral ICA） and non-negative constrained decomposition,a moving time window is introduced,and multiple dominant spectral components are extracted within the short-time window.Various trend illustrations are presented,and significant-index plots to indicate the degrees of contribution of process variables are given.The proposed approach can effectively monitor the changing trends of the dominant frequency and energy as well as the process variable contributions,and is feasible for non-stationary process monitoring,fault detection and localization.Simulation results demonstrate the feasibility of the proposed method.