In industrial processes,there exist faults that have complex effect on process variables.Complex and simple faults are defined according to their effect dimensions.The conventional approaches based on structured residuals cannot isolate complex faults.This paper presents a multi-level strategy for complex fault isolation.An extraction procedure is employed to reduce the complex faults to simple ones and assign them to several levels.On each level,faults are isolated by their different responses in the structured residuals.Each residual is obtained insensitive to one fault but more sensitive to others.The faults on different levels are verified to have different residual responses and will not be confused.An entire incidence matrix containing residual response characteristics of all faults is obtained,based on which faults can be isolated.The proposed method is applied in the Tennessee Eastman process example,and the effectiveness and advantage are demonstrated.
针对化工生产中日益增多的间歇过程,提出了一种基于多元统计信号处理的过程监控方法,其主要思想为将过程信息空间划分为由盲源信号描述的信号子空间、过程主元描述的信号子空间和残差信号子空间,随后对各个信号子空间构造过程统计量或分类器提取信号特征进行过程监控,该方法避免了传统多元统计过程控制(mult-ivariate statistical process contro,lMSPC)需假设过程特征信号服从正态分布的前提.将本方法与传统MSPC方法的性能进行了对比,并在仿真中给出了对比研究结果.通过对间歇过程的仿真研究表明,该方法不仅能够有效地检测出故障,而且有利于故障的分离和定位,从而说明该方法不仅是有效的,而且其性能优于仅能检测故障的传统MSPC过程监控方法.
Multiscale classification has potential advantages for monitoring industrial processes generally driven by events in different time and frequency domains.In this study, we adopt stationary wavelet transform for multiscale analysis and propose an applicable scale selection method to obtain the most discriminative scale features.Then using the multiscale features, we construct two classifiers:(1) a supported vector machine(SVM) classifier based on classification distance, and(2) a Bayes classifier based on probability estimation.For the SVM classifier, we use 4-fold cross-validation and grid-search to obtain the optimal parameters.For the Bayes classifier, we introduce dimension reduction techniques including kernel Fisher discriminant analysis(KFDA) and principal component analysis(PCA) to investigate their influence on classification accuracy.We tested the classifiers with two simulated benchmark processes:the continuous stirred tank reactor(CSTR) process and the Tennessee Eastman(TE) process.We also tested them on a real polypropylene production process.The performance comparison among the classifiers in different scales and scale combinations showed that when datasets present typical scale features, the multiscale classifier had higher classification accuracy than conventional single scale classifiers.We also found that dimension reduction can generally contribute to a better classification in our tests.
Yu-ming LIU Lu-bin YE Ping-you ZHENG Xiang-rong SHI Bin HU Jun LIANG
