文献类型：期刊文献

英文题名：Robust monitoring of industrial processes using process data with outliers and missing values

作者：Luo, Lijia[1];Bao, Shiyi[1];Peng, Xin[2]

机构：[1]Zhejiang Univ Technol, Inst Proc Equipment & Control Engn, Hangzhou 310014, Zhejiang, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2019

卷号：192

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616417236);WOS:【SCI-EXPANDED(收录号:WOS:000488654700005)】；

基金：This study was supported by the National Natural Science Foundation of China (no. 61304116).

语种：英文

外文关键词：Robust estimation; Cellwise outliers; Missing data; Process monitoring; Fault detection and diagnosis

摘要：The quality of process data has great effect on the performance of data-driven process monitoring methods. Two main problems that reduce the quality of data are the contamination (i.e., outliers) and missing values. To address these problems, a two-step method called COF-GRE is developed for the robust estimation of multivariate location and scatter matrix in the presence of missing data and both casewise and cellwise outliers. The first step applies cellwise outlier filters (COF) to identify and remove the contaminated cells in a multivariate data matrix. In the second step the generalized Rock S-estimators (GRE) are used to handle casewise outliers and missing values simultaneously. Based on the COF-GRE method, a robust process monitoring method is then developed to achieve high-performance monitoring even if the reference data of industrial processes contain outliers and missing values. A fault detection index, called the robust T-2 statistic, is defined using the multivariate location and scatter matrix obtained by COF-GRE. A fault diagnosis method is also proposed to determine faulty variables and at the same time to compute their fault magnitudes. The effectiveness and advantages of the proposed methods are illustrated with a simulation example and an industrial case study.