基于主元子空间故障重构技术的故障诊断研究  

Fault diagnosis research based on fault reconstruction in principal component subspace

作　　者：肖应旺[1] 姚美银[1] 

机构地区：[1]华南师范大学南海校区计算机工程系,广东佛山528225

出　　处：《计算机与应用化学》2008年第1期93-98,共6页Computers and Applied Chemistry

基　　金：教育部基础条件平台建设--工业微生物资源数据库(505006)

摘　　要：针对基于主元分析(PCA)的统计性能监控法,由于不用过程机理模型的信息,因此,对故障诊断问题有难以在理论上作系统分析的缺陷,于是提出了一种基于主元子空间故障重构技术的故障诊断方法。利用故障子空间的概念,在故障重构技术的基础上,研究基于T^2统计量的故障诊断问题,提出故障识别指标和诊断算法。通过对双效蒸发过程的仿真监测,验证该诊断方法的有效性。The most significant advantage of principal component analysis （PCA） is that no precise process model is needed. Nevertheless, because the first principle model information is not utilized by statistical monitoring approach based on PCA, the development of capacity for fault diagnosis is restricted to some extent. A fault diagnosis approach based on fault reconstruction in principal component subspace is proposed. Based on the concept on fault subspace and the fault reconstruction technology, the fault diagnosis issue is explored by using T^2 index and the fault identification index and diagnosis arithmetic are presented. The fault diagnosis approach is then illustrated and verified by monitoring a simulated double-effect evaporator process, the simulation results show that the acquired theoretical results can effectively identify fault （sensor and process fault） and are effective.

关 键 词：主元子空间 故障子空间 故障重构 故障诊断 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]