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multivariate statistical process monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation

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Chinese Journal of Chemical Engineering 2007年第4期15卷 524-532页

作者：熊丽梁军钱积新 National Lab of Industrial Control Technology Institute of Systems Engineering Zhejiang University Hangzhou310027 China

Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, *** .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.

关键词： multivariate statistical process monitoring principal comPonent analysis kermel density estimation polypropylene catalyzer reactor fault detection data-driven tools

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multivariate statistical process monitoring of Batch-to-Batch Startups

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AICHE JOURNAL 2015年第11期61卷 3719-3727页

作者： Yan, Zhengbing Huang, Bi-Ling Yao, Yuan Wenzhou Univ Coll Phys & Elect Informat Engn Dept Elect Engn & Automat Wenzhou 325035 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 30013 Taiwan

In batch processes, multivariate statistical process monitoring (MSPM) plays an important role for ensuring process safety. However, despite many methods proposed, few of them can be applied to batch-to-batch startups. The reason is that, during the startup stage, process data are usually nonstationary and nonidentically distributed from batch to batch. In this article, the trajectory signal of each process variable is decomposed into a series of components corresponding to different frequencies, by adopting a nonparametric signal decomposition technique named ensemble empirical mode decomposition. Then, through instantaneous frequency calculation, these components can be divided into two groups. The first group reflects the long-term trend between batches, which extracts the batch-wise nonstationary drift information. The second group corresponds to the short-term intrabatch variations. The variable trajectory signals reconstructed from the latter fulfills the requirements of conventional MSPM. The feasibility of the proposed method is illustrated using an injection molding process. (c) 2015 American Institute of Chemical Engineers

关键词： multivariate statistical process monitoring batch process ensemble empirical mode decomposition instantaneous frequency signal decomposition

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Modified non-Gaussian multivariate statistical process monitoring based on the Gaussian distribution transformation

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JOURNAL OF process CONTROL 2020年 85卷 1-14页

作者： Du, Wenyou Zhang, Yingwei Zhou, Wei Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

Independent component analysis (ICA) has been applied for non-Gaussian multivariate statistical process monitoring (MSPM) for several years. As the independent components do not satisfy the multivariate Gaussian distribution, a missed alarm occurs when monitoring with traditional statistics. In this paper, we propose a Gaussian distribution transformation (GDT)-based monitoring method. Independent components are first transformed into approximate Gaussian distributions through the proposed nonlinear mapping. Then, we propose new statistics and their control limits to reduce missed alarms. The proposed method is particularly suitable for slight magnitude fault and early-stage fault detection. The ratio part of the area above the curve (RPAAC) is developed to evaluate the performance in fault detection. The experimental results from a synthetic example show the effectiveness of our proposed method. We also apply our method to monitor an electrical fused magnesia furnace (EFMF), and eruption and furnace wall melt faults can be detected in time. (C) 2017 Published by Elsevier Ltd.

关键词： multivariate statistical process monitoring Independent component analysis Gaussian distribution transformation Electrical fused magnesia furnace

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Root cause analysis in multivariate statistical process monitoring: Integrating reconstruction-based multivariate contribution analysis with fuzzy-signed directed graphs

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COMPUTERS & CHEMICAL ENGINEERING 2014年 64卷 167-177页

作者： He, Bo Chen, Tao Yang, Xianhui Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Surrey Dept Chem & Proc Engn Guildford GU2 7XH Surrey England State Grid Energy Res Inst Beijing 102209 Peoples R China

Root cause analysis is an important method for fault diagnosis when used with multivariate statistical process monitoring (MSPM). Conventional contribution analysis in MSPM can only isolate the effects of the fault by pinpointing inconsistent variables, but not the underlying cause. By integrating reconstruction-based multivariate contribution analysis (RBMCA) with fuzzy-signed directed graph (SDG), this paper developed a hybrid fault diagnosis method to identify the root cause of the detected fault. First, a RBMCA-based fuzzy logic was proposed to represent the signs of the process variables. Then, the fuzzy logic was extended to examine the potential relationship from causes to effects in the form of the degree of truth (DoT). An efficient branch and bound algorithm was developed to search for the maximal DoT that explains the effect, and the corresponding causes can be identified. Except for the need to construct an SDG for the process, this method does not require historical data of known faults. The usefulness of the proposed method was demonstrated through a case study on the Tennessee Eastman benchmark problem. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Fuzzy logic multivariate statistical process monitoring Reconstruction-based multivariate contributions Signed directed graph

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An intelligent system for multivariate statistical process monitoring and diagnosis

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ISA TRANSACTIONS 2002年第2期41卷 255-270页

作者： Tatara, E Çinar, A IIT Dept Chem & Environm Engn Chicago IL 60616 USA

A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present. (C)2002 ISA-The Instrumentation, Systems, and Automation Society.

关键词： on-line process monitoring real-time monitoring knowledge-based systems multivariate statistical process monitoring sensor auditing data fusion canonical variate analysis state space models

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New Method for multivariate statistical process monitoring

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Journal of Beijing Institute of Technology 2010年第1期19卷 92-98页

作者：裴旭东陈祥光刘春涛 School of Chemical Engineering and Environment Beijing Institute of Technology

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more *** discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault *** control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature *** control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault *** to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.

关键词： Fisher discriminant analysis individuals control chart multivariate statistical process monitoring

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In-Depth Evaluation of Data Collected During a Continuous Pharmaceutical Manufacturing process: A multivariate statistical process monitoring Approach

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JOURNAL OF PHARMACEUTICAL SCIENCES 2019年第1期108卷 439-450页

作者： Silva, Ana F. Vercruysse, Jurgen Vervaet, Chris Remon, Jean P. Lopes, Joao A. De Beer, Thomas Sarraguca, Mafalda C. Univ Ghent Lab Pharmaceut Proc Analyt Technol B-9000 Ghent Belgium Univ Porto Fac Farm Dept Ciencias Quim LAQV REQUIMTE Rua Jorge Viterbo Ferreira 228 P-4050313 Porto Portugal Univ Ghent Lab Pharmaceut Technol B-9000 Ghent Belgium Univ Lisbon Fac Farm Res Inst Med iMed Lisboa Ave Prof Gama Pinto P-1649003 Lisbon Portugal

The present work presents an in-depth evaluation of continuously collected data during a twin-screw granulation and drying process performed on a continuous manufacturing line. During operation, the continuous line logs 49 univariate process variables, hence generating a large amount of data. Three identical 5-h continuous manufacturing runs were performed. multivariate data analysis tools, more specifically latent variable modeling tools such as principal component analysis, were used to extract information from the generated data sets unveiling process trends and drifts. Furthermore, a statistical process monitoring strategy is presented. The approach is based on the application of multivariate statistical process monitoring to model the variables that remain around a steady state. (C) 2019 Published by Elsevier Inc. on behalf of the American Pharmacists Association.

关键词： multivariate statistical process monitoring principal component analysis in-process monitoring continuous manufacturing

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Abnormal Identification of Dissolved Gas in Oil monitoring Device Based on multivariate statistical process monitoring 12

Abnormal Identification of Dissolved Gas in Oil Monitoring D...

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12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM)

作者： Zhang, Peng Rao, Wei Qi, Bo Wang, Yiming Rong, Zhihai Li, Chengrong North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Global Energy Interconnect Res Inst Co Ltd Adv Comp & Big Data Lab SGCC Beijing 102209 Peoples R China

ISBN: (纸本)9781538657881

The on-line monitoring of dissolved gas in transformer oil can effectively reflect the abnormal state of transformer. However, abnormal operation of the monitoring equipment will greatly interfere with abnormal transformer identification. It is difficult to identify the abnormal operation of dissolved gas monitoring device. This paper introduces a method of processing of data-driven and presents an anomaly identification method for dissolved gas monitoring device in oil based on multivariate statistical process monitoring(MSPM). Firstly, MSPM abnormal identify model which based on PCA is established. This model can illustrate the data characteristics of the system under normal conditions. Then, identify abnormal data based on F statistic and SPE statistic. F can reflect most of the information of the principal component subspace, and is more suitable for the case of strong white noise. residual subspace SPE can handle the invisible information of the principal component subspace. It is found that the T-2 statistic and the SPE statistic are sensitive to the variation of characteristic gas. Finally, the abnormal work data library of monitoring device is established to identify the abnormal devices. This paper collects historical on-line monitoring data of a regional power grid to build a library, and predict device abnormal situation criterion based on this library. It listed the connection between common features of data and abnormal reason. The cross interference of monitoring equipment and overheat fault of transformer are correctly identifying to justify the method. This device can distinguish the abnormal state and detect transformer insulation cracking.

关键词： high-dimensional data DGA on-line testing abnormal monitoring equipment multivariate statistical process monitoring

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The application of multivariate statistical process monitoring in non-industrial processes

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QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2018年第4期15卷 526-549页

作者： Bersimis, Sotiris Sgora, Aggeliki Psarakis, Stelios Univ Piraeus Dept Stat & Insurance Sci Piraeus Greece Athens Univ Econ & Business Dept Stat Athens Greece

statistical process monitoring (SPM) techniques have been widely used in industry for many decades in order to assess the process stability, as well as the final product quality. Interestingly, SPM techniques have gained popularity in many non-industrial fields providing even more opportunities for research. In this paper, we consider the case of multivariate statistical process monitoring (MSPM) and perform an extensive literature review on MSPM applications in non-industrial fields. More specifically, the aim of this paper is to point out the main non-industrial application fields of MSPM that have appeared in the literature, discuss the main challenges that arise upon applying MSPM outside the industrial environment and provide some thoughts for the future of MSPM outside industry.

关键词： multivariate statistical process monitoring MSPC hotelling T-2 control charts control charts MEWMA MCUSUM non-industrial process monitoring

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Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring

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JOURNAL OF process CONTROL 2000年第4期10卷 341-350页

作者： Norvilas, A Negiz, A DeCicco, J Çinar, A IIT Dept Environm Chem & Engn Chicago IL 60616 USA

An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate stare space (CVSS) process models. Fault detection is based on T-2 charts of state variables. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T-2 values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This intelligent monitoring and diagnosis system can be used to monitor multivariable processes with autocorrelated, cross-correlated, and collinear data. The structure of the integrated system is described and its performance is illustrated by simulation studies. (C) 2000 Elsevier Science Ltd. All rights reserved.

关键词： multivariate statistical process monitoring fault detection and diagnosis canonical variate analysis knowledge-based system state space models

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