控制理论与应用2012，Vol.29Issue(6)：754-764,11.
基于核主元分析-主元分析的多阶段间歇过程故障监测与诊断
Fault detection and diagnosis of multiphase batch process based on kernel principal component analysis-principal component analysis
摘要
Abstract
Fault detection in multiple phase processes is a complicated problem, because it is needed in both the steady phase and the transition from phase to phase. To overcome the hard-partition and misclassification problems, and also to monitor batch processes more accurately and efficiently, we propose a novel strategy for fault monitoring and diagnosing in batch processes based on the kernel principal component analysis-principal component analysis (KPCA-PCA). In this work, a phase division algorithm is designed based on the similarity index between different time-slice data matrices of batch processes, following by a fuzzy membership grade transition identification step. The steady phase ranges and the transition ranges are then modeled by PCA with time-varying covariance structures and KPCA separately. Results of simulation study and industrial application to penicillin fermentation process clearly demonstrate the effectiveness and feasibility of the proposed method, which detects various faults more promptly with desirable reliability.关键词
主元分析/核主元分析/故障诊断/间歇过程Key words
principal component analysis/ kernel principal component analysis/ fault diagnosis/ batch process分类
信息技术与安全科学引用本文复制引用
齐咏生,王普,高学金..基于核主元分析-主元分析的多阶段间歇过程故障监测与诊断[J].控制理论与应用,2012,29(6):754-764,11.基金项目
国家自然科学基金资助项目(61174109,60704036) （61174109,60704036）
高等学校博士学科点专项科研基金资助项目(20101103110009) （20101103110009）
内蒙古工业大学科学研究资助项目(ZS0201037). （ZS0201037）
