摘要
针对间歇过程的故障诊断问题, 提出了一种新的混合模型方法———MPCA MDPLS. 这种方法包括两个模型: 多向主元分析 (MPCA) 模型和多向判别部分最小二乘 (MDPLS) 模型. 这两个模型的建模数据不仅包括正常工况的数据, 而且还包含了各种已知故障数据. 因此, MPCA模型具有检测未知故障的能力. 给出了 MD PLS模型故障诊断限, 对经MPCA模型检测不是未知故障的故障做进一步诊断. 如果故障是未知的, 可以采取其他的方法来分析新的故障, 并按不同类别存入到数据库中. 当多次出现这种故障之后 (一般≥5 次), 把新的故障数据加入到建模数据中, 并重新建立 MPCA -MDPLS模型. 通过对实际工业链霉素发酵过程数据的分析,表明了提出的算法是可行的、有效的, 并具有识别未知新故障的能力.
In order to diagnose faults for batch processes, a novel method, MPCA-MDPLS model, is presented in this paper. The proposed method includes two models: MPCA (multiway principal component analysis) model and MDPLS (multiway discriminant partial least squares) model. Based on data collected from the plant during normal operation and specific faults, two models are constructed. The MPCA model can detect unknown faults. The faults, which are detected by using MPCA model as not unknown, further diagnosed by the MDPLS model. If it is identified as unknown, the root cause is analyzed by using various methods. The unknown fault is then saved in the historical database in order to reconstruct the MPCA-MDPLS model. The method is proved to be feasible and effective by the application in diagnosing a multi-stage streptomycin fermentation process.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2005年第3期482-486,共5页
CIESC Journal
基金
国家高技术研究发展计划项目 (2001AA413110).~~
关键词
间歇过程
主元分析
判别部分最小二乘
故障诊断
Database systems
Fermentation
Identification (control systems)
Least squares approximations
Principal component analysis
