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第31届中国过程控制会议（CPCC2020）

作者： Feng Yu Weiyang Li Fan Yang Dexian Huang Zhihua Xiong Department of Automation Tsinghua University Beijing National Research Center for Information Science and Technology

Causality analysis methods, such as Granger causality analysis(GCA) and transfer entropy(TE) have been widely used for fault detection and topology building in process monitoring. However, they view causal factors as variables while combinations of variables which may exert more causal influence on target variables are usually ignored. In this article, the dynamic-inner partial least squares(DiPLS) method is introduced and extended to causal feature extraction(CFE) methods. Taking advantage of the dynamic-inner relationship which is consistent with causality in DiPLS, CFE tries to extract combinations of process variables whose current information and past information exert more causal influence than any other combinations of process variables. Once the causal relationship is determined, process monitoring based on CFE should differ faults where causal relationship is changed from those faults where causal relationship remains the same. The effectiveness of the proposed method is shown through applications in simulated data and the Tennessee Eastman process.

关键词： Causal Feature dynamic-inner model process monitoring

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Specific index-related process monitoring using a two-step information extraction method

Specific index-related process monitoring using a two-step i...

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第30届中国控制与决策会议

作者： Bo Zhao Bing Song Shuai Tan Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical processes of Ministry of Education East China University of Science and Technology

Specific index-related process monitoring covers a wide range of requirements from industrial production. At present, it is still a challenge to divide into the specific index-related information and the specific index-unrelated information more accurately. In this paper, a two-step information extraction method is presented to solve this problem. In the first step, the overall process variables are separated into a strongly related variable set and a weakly related variable set. In the second step, the two sets are further divided into a specific index-related subset and a specific index-unrelated subset, respectively. Two specific index-related subsets form a new subspace related to the specific index. Two specific index-unrelated subsets are combined into a specific index-unrelated subspace. Two 2 T statistics are built in the specific index-related subspace and the specific index-unrelated subspace, respectively. Finally, the proposed method is applied for Tennessee Eastman（TE）. The results indicate the effectiveness of the proposed method.

关键词： specific index process monitoring subspace information extraction

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Distribution Adaptation Local Outlier Factor for Multimode process monitoring

Distribution Adaptation Local Outlier Factor for Multimode P...

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第三十九届中国控制会议

作者： Yutang Xiao Yang Tao Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical processes of the Ministry of Education East China University of Science and Technology

In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex. It is difficult to monitor multimode processes accurately by using traditional Multivariate Statistical process monitoring（MSPM） methods. In this case, the absence of labeled data is considered more valuable. This paper proposes a new unsupervised domain adaptive method called the Distribution Adaptation Local Outlier Factor（DALOF）. This method can extract useful information from multimode data through domain adaptation, which can reduce the distribution difference of training data and improve the accuracy of modeling. In the DALOF model, data from different modes in the training set are defined as the source domain and the target domain. Weight the training data according to their correlation, and then project these data into a low-dimensional subspace. In this space, the distribution distance between the source and target domain is minimum. To solve the nonlinear characteristics of multimode data, this paper uses LOF to build a model and formed a density-based monitoring index. Numerical examples and Tennessee Eastman（TE） process simulation demonstrate the effectiveness of DALOF.

关键词： Distribution Adaptation Local Outlier Factor Multimode process process monitoring

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A process monitoring Method Based on Global-Local Structure Analysis in Principal Component Reconstruction Space

A Process Monitoring Method Based on Global-Local Structure ...

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第37届中国控制会议

作者： Qi Chen Canghua Jiang Siyi Wu School of Electrical Engineering and Automation Hefei University of Technology

The performance of projection-based process monitoring methods is prone to be affected by noise in data. By constructing latent variables that have large contribution to variance, principal component analysis（PCA） can separate the main information from noise. Thus, it is widely used in data-based process monitoring and signal denoising. Global-local structure analysis（GLSA） can extract both global information and local structure information by combining PCA and local preserving projection. However, GLSA cannot avoid the possibility to model noise information. In this paper, a novel method is proposed which applies GLSA in the data reconstruction space of principal components. This method can not only extract global variance information and local structure information but also avoid the possibility of noise modeling. Simulations based on data from a numerical example and TE process demonstrate that the proposed method is superior to GLSA for fault detection.

关键词： process monitoring Principal component analysis Global-local structure analysis Data reconstruction Feature extraction

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Multimode Industrial process monitoring using Hierarchical Mode Division and Serial Independent Component Analysis

Multimode Industrial Process Monitoring using Hierarchical M...

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第32届中国过程控制会议(CPCC2021)

作者： Shuai Li Xiaofeng Zhou Haibo Shi Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences

With the increasing scale and complexity,how to analyze the hybrid characteristics including multimode,nonGaussianity and nonlinearity is one of the most difficult problems faced by multimode industrial process *** paper proposes a monitoring method using hierarchical mode division with serial independent component analysis for multimode processes with *** the multimodal uncertainty and transition mode,hierarchical mode information with transitions is first obtained by mode division using hierarchical density peaks ***,by integrating independent component analysis(ICA) and kernel independent component analysis(KICA) using the serial structure,a hybrid non-Gaussian and nonlinear process modeling method using serial independent component analysis is used for hierarchical process ***,the stable and transition mode identification using geodesic distance is developed for multimode industrial process *** monitoring results for the penicillin fermentation process show the feasibility and effectiveness.

关键词： process monitoring multimode industrial process transition mode hierarchical mode division serial independent component analysis

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A Novel Outlier Detection Method for Improving Industrial process monitoring

A Novel Outlier Detection Method for Improving Industrial Pr...

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第30届中国控制与决策会议

作者： Yanfeng Zhang Liaoning Water Conservancy Vocational College

This paper focuses on improving data environment in the context of industrial process *** the negative influence of outliers in target set,this paper proposes a novel two-level ensemble model,in which the base learner is *** data processed by our method,performance of data-based techniques dedicating to process monitoring can be improved *** Tennessee Eastman process is used to verify the significance of our method in terms of process *** results approve the potential and necessity of our method.

关键词： anomaly detection one-class classification ensemble process monitoring

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Research on process monitoring Method Based on SPC and PCA Technology

Research on Process Monitoring Method Based on SPC and PCA T...

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2011 Chinese Control and Decision Conference（CCDC）

作者： Kunlin Zhou,School of Mechanical,Electrical & Information Engineering,Shandong University at Weihai,Weihai,264209 Rongsheng Guo,School of Mechanical,Electrical & Information Engineering,Shandong University at Weihai,Weihai,264209

In order to overcome the interaction between variables for the multivariate process monitoring,a new method to use statistical process control and principal component analysis was proposed,which reduced the multivariate process data by the matrix *** can extract the main features of the process variable,and set up the upper and lower monitoring limits for the production process,and change the real-time data of the multi-variable into the monitoring information of an integrated process,and present effectively the information to the *** this method,it can eliminate the interaction between variables and provide the information quickly and efficiently for the fault diagnosis of production *** on-line monitoring system was designed for the distillation process as an example,and the effectiveness of this method was illustrated.

关键词： interaction process monitoring SPC PCA monitoring limits fault diagnosis

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Design of the process monitoring Management System Based on RFID

Design of the Process Monitoring Management System Based on ...

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2009中国控制与决策会议

作者： Ma Lian-bo~1,Zhang Lei~1,Ma Lian-yang~2,Hu Kun-yuan~1,Su Wei-xing~1 1.Key Laboratory of Industrial Informatics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 100004,China 2.Department of Computer,Xidian University,Xi''an 710071,China

＜正＞Considering the higher requirements for data acquisition and information processing of process-trace in discrete manufacturing industry,the architecture of the process monitoring management system based on RFID is *** implementation mechanism of the application agent structure is presented as well as the complex logic controlling *** progress analysis model is also *** last,the implementation of the intelligent terminal is given,which proves the feasibility and the effectiveness of the system in practical application.

关键词： Multi-mobile Agent RFID process monitoring

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process disturbances monitoring and recognition of short-circuiting GMAW by fuzzy c-means system

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China Welding 2011年第4期20卷 28-33页

作者：胡庆贤王顺尧王艳辉 Provincial Key Lab of Advanced Welding Technology Jiangsu University of Science and Technology Zhenjiang 212002

An intelligent fuzzy c-means system for process monitoring and recognition of process disturbances during short- circuiting gas metal arc welding （GMAW） is introduced in this paper. The raw measured and statistically test data of probability density distribution （ PDD ） and class frequency distribution （ CFD ） of welding electrical parameters are further processed into a 7-dimensional array which is designed to describe various welding conditions, and is employed as input vector of the intelligent fuzzy c-means system. The fuzzy c-means system is used to conduct process monitoring and automatic recognition. The correct recognition rate of 24 test data under 8 kinds of welding condition is 92%.

关键词： process monitoring fuzzy c-means process disturbance short-circuiting gas metal arc welding

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A Flow-based Deep Variational Canonical Variate Analysis Method for monitoring Blast Furnace Iron-making process

A Flow-based Deep Variational Canonical Variate Analysis Met...

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第35届中国过程控制会议

作者： Yuelin Yang Chunjie Yang Siwei Lou Xiongzhuo Zhu Weibin Wang College of Control Science and Engineering Zhejiang University

Blast furnace iron-making process is an essential component in the iron and steel industry. However, establishing an efficient process monitoring model remains a significant challenge due to dynamic and nonlinear qualities, coupled with substantial noise and outliers. This paper presents a flow-based deep variational canonical variate analysis(FDVCVA) method as a solution to overcome the aforementioned challenges. First, FDVCVA model is designed by incorporating the variational auto-encoder(VAE), flow-based model, and canonical variate analysis(CVA). Further, the statistic is founded on the results of FDVCVA and control limit is calculated using the kernel density estimation(KDE) technique. Finally, the utilization of practical blast furnace data serves to verify the efficiency of the proposed method.

关键词： Blast furnace iron-making process canonical variate analysis flow-based model process monitoring

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