Dynamic Non-Gaussian hybrid serial modeling for industrial process monitoring | |
Li S(李帅)1,2,3,4; Zhou XF(周晓锋)1,2,3; Shi HB(史海波)1,2,3; Pan FC(潘福成)1,2,3 | |
刊名 | Chemometrics and Intelligent Laboratory Systems |
2021 | |
卷号 | 216页码:1-15 |
关键词 | Bayesian inference Dynamic non-Gaussian hybrid serial modeling Hybrid serial similarity factor Multivariate non-Gaussianity evaluation Process monitoring |
ISSN号 | 0169-7439 |
产权排序 | 1 |
英文摘要 | Process monitoring has been widely used for fault detection and performance supervision in modern industrial processes. Nevertheless, hybrid characteristics including Gaussianity, non-Gaussianity and dynamic usually coexist in process variables, which brings new challenge to obtain satisfactory monitoring performance. Aiming at the hybrid characteristics problem, this paper proposes a dynamic non-Gaussian hybrid serial modeling method for industrial process monitoring. First, a multivariate non-Gaussianity evaluation method is utilized to divide industrial process variables into the Gaussian variable subspace and the non-Gaussian variable subspace. Afterwards considering the hybrid characteristics including Gaussianity, non-Gaussianity and dynamic at information level, a dynamic principal component analysis (DPCA)-dynamic independent component analysis (DICA)-based hybrid serial modeling method is presented for analyzing simultaneously the dynamic Gaussian and non-Gaussian information in each variable subspace. Subsequently, the final monitoring results are obtained using Bayesian inference and the DPCA-DICA-based hybrid serial similarity factor is proposed for fault identification. Unlike the existing methods, the proposed method analyzes simultaneously the Gaussianity, non-Gaussianity and dynamic at different levels of variable and information for improving performance. The case studies including a numerical system, the Tennessee Eastman process and a practical industrial process demonstrate its feasibility and effectiveness. |
资助项目 | Natural Science Foundation of Liaoning Province, China[2019-MS-344] |
WOS关键词 | INDEPENDENT COMPONENT ANALYSIS ; FAULT-DETECTION ; BAYESIAN METHOD ; ICA-PCA ; DIAGNOSIS ; DIVISION |
WOS研究方向 | Automation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics |
语种 | 英语 |
WOS记录号 | WOS:000693429200005 |
资助机构 | Natural Science Foundation of Liaoning Province, China (2019-MS-344). |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29319] |
专题 | 沈阳自动化研究所_数字工厂研究室 |
通讯作者 | Li S(李帅); Zhou XF(周晓锋) |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 4.University of Chinese Academy of Sciences, Beijing 100049, China |
推荐引用方式 GB/T 7714 | Li S,Zhou XF,Shi HB,et al. Dynamic Non-Gaussian hybrid serial modeling for industrial process monitoring[J]. Chemometrics and Intelligent Laboratory Systems,2021,216:1-15. |
APA | Li S,Zhou XF,Shi HB,&Pan FC.(2021).Dynamic Non-Gaussian hybrid serial modeling for industrial process monitoring.Chemometrics and Intelligent Laboratory Systems,216,1-15. |
MLA | Li S,et al."Dynamic Non-Gaussian hybrid serial modeling for industrial process monitoring".Chemometrics and Intelligent Laboratory Systems 216(2021):1-15. |
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