摘要
基于传统的多向主元分析MPCA(multiway principal component analysis)常会导致误诊断,且对批过程难以保证在线状态监测和故障诊断的实时性,提出了一种基于特征子空间的滑动窗主元分析方法。在实时故障监测与诊断时,该方法采用适当大小的滑动窗逐步更新当前子数据空间,对当前子数据空间故障的识别通过依次计算其与基底库中各故障的匹配度来进行。这种方法克服了传统的MPCA不能处理非线性过程和实时性问题,并避免了MPCA在线应用时预报未来测量值带来的误差, 提高了批过程性能监测和故障诊断的准确性。
A characteristic subspace moving window principal component analysis for on-line batch process monitoring and fault detection was proposed. Using proper moving window to update current data subspace and calculating matching degree between the current data subspace and each fault belonged to fundus warehouse step by step, this approach recognizes the current data subspace fault and emphasizes particularly on-line process performance monitoring and exactly fault detecting which results in extraordinary behavior of batch processes.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2006年第4期303-306,共4页
Computers and Applied Chemistry
基金
国家863资助项目(2002AA217131)
关键词
主元分析
特征子空间距离
滑动窗口
批过程
故障诊断
principal component analysis, characteristic subspace distance, moving window, batch process, fault detection
