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

中文关键词  主元分析  核主元分析  故障诊断  间歇过程

英文关键词  principal component analysis  kernel principal component analysis  fault diagnosis  batch process

中文摘要

      具有过渡特性的多阶段间歇过程故障监测是一个复杂的问题, 既需要考虑稳定阶段下的故障监测, 也需要考虑不同阶段间的过渡故障监测. 为克服传统硬划分方法导致误警和漏报率高的缺陷, 同时也为实现更加精确、有效的故障监测与诊断, 提出一套完整的基于核主元分析–主元分析(KPCA–PCA)的多阶段间歇过程故障监测与诊断策略. 该方法依据数据相似度实现阶段划分, 定义模糊隶属度辨识相邻阶段间的过渡, 最后对稳定阶段和过渡过程分别建立具有时变协方差的PCA和KPCA故障监测与诊断模型. 通过对青霉素发酵过程的仿真平台及工业应用研究表明, 该方法具有更可靠的监控性能, 能及时、准确的检测出过程中存在的异常情况.

英文摘要

      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.