文献类型：期刊文献

英文题名：Multimode Process Monitoring Using Variational Bayesian Inference and Canonical Correlation Analysis

作者：Jiang, Qingchao[1];Yan, Xuefeng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2019

卷号：16

期号：4

起止页码：1814

外文期刊名：IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

收录：;EI(收录号：20200207992105);WOS:【SCI-EXPANDED(收录号:WOS:000492428500029)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61603138 and Grant 21878081, in part by Shanghai Pujiang Program under Grant 17PJD009, in part by Fundamental Research Funds for the Central Universities under Grant 222201717006 and Grant 222201714027, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Monitoring; Bayes methods; Correlation; Fault detection; Indexes; Gaussian mixture model; Canonical correlation analysis (CCA); fault detection; multimode process monitoring; variational Bayesian Gaussian mixture model (GMM)

摘要：Industrial processes generally have various operation modes, and fault detection for such processes is important. This paper proposes a method that integrates a variational Bayesian Gaussian mixture model with canonical correlation analysis (VBGMM-CCA) for efficient multimode process monitoring. The proposed VBGMM-CCA method maximizes the advantage of VBGMM in automatic mode identification and the superiority of CCA in local fault detection. First, VBGMM is applied to unlabeled historical process data to determine the number of operation modes and cluster the data in each mode. Second, local CCA models that explore input and output relationships are established. Fault detection residuals are generated in each local CCA model, and monitoring statistics are derived. Finally, a Bayesian inference probability index that integrates monitoring results from all local models is developed to increase the monitoring robustness. The effectiveness of the proposed monitoring scheme is verified through experimental studies on a numerical example and the multiphase batch-fed penicillin fermentation process. Note to Practitioners-Process monitoring is important in guaranteeing process safety and improving product quality. Large amounts of unlabeled process data with multiple operation modes generally exist in industrial processes. Labeling these data is difficult or costly. Hence, this paper presents a VBGMM-CCA method for monitoring multimode processes. The key advantage of the proposed method is that it automatically identifies the number of operation modes in historical data and clusters the data. Then, local CCA monitors are established to model the process input and output relationships. During online monitoring, the running-on operation mode is identified through a density function, and the process status is evaluated by the corresponding CCA monitor. A probabilistic monitoring index is also developed to increase the robustness of the monitoring. In comparison with the results of conventional methods, the monitoring results of the proposed approach are more reliable and informative because the process status and the type of the detected fault are presented.