1
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Dynamic plant-wide process monitoring based on distributed slow feature analysis with inter-unit dissimilarity. KOREAN J CHEM ENG 2022. [DOI: 10.1007/s11814-021-0901-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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2
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Hsu CC, Shih PC, Tien FC. Integrate weighted dependence and skewness based multiblock principal component analysis with Bayesian inference for large-scale process monitoring. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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3
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4
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He YL, Zhao Y, Zhu QX, Xu Y. Online Distributed Process Monitoring and Alarm Analysis Using Novel Canonical Variate Analysis with Multicorrelation Blocks and Enhanced Contribution Plot. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02209] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yang Zhao
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yuan Xu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
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5
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Cui Q, Li S. Process monitoring method based on correlation variable classification and vine copula. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23702] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Qun Cui
- Key Laboratory of Advanced Control and Optimization for Chemical ProcessesMinistry of Education, East China University of Science and Technology Shanghai China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical ProcessesMinistry of Education, East China University of Science and Technology Shanghai China
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6
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Ma L, Dong J, Peng K. A novel key performance indicator oriented hierarchical monitoring and propagation path identification framework for complex industrial processes. ISA TRANSACTIONS 2020; 96:1-13. [PMID: 31196562 DOI: 10.1016/j.isatra.2019.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 06/02/2019] [Accepted: 06/03/2019] [Indexed: 06/09/2023]
Abstract
As the first protective layer for modern complex industrial processes, process monitoring and fault diagnosis (PM-FD) systems play a vital role in ensuring product quality, overall equipment effectiveness and process safety, which have recently become one of the hotspots both in academic research and practical application domains. Different from previous frameworks, this paper dedicates on industrial practices and theoretical methods for hierarchical monitoring and propagation path identification of key performance indicator (KPI) oriented faults in complex industrial processes, which can not only help field engineers to timely and purposefully keep track of the state of the process, but also help them to take appropriate remedial actions to remove the abnormal behaviors from the process. For these purposes, firstly, a new data-driven gap metric approach is proposed for monitoring KPI oriented faults in the block level. Then, Bayesian fusion is implemented to form monitoring decisions from the plant-wide level. After that, a neural network architecture-based Granger causality analysis method is developed for propagation path identification of KPI oriented faults. Finally, the proposed methods are validated in Tennessee Eastman process, where detailed simulation processes are presented and better performance is shown compared with the existing approaches.
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Affiliation(s)
- Liang Ma
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.
| | - Jie Dong
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.
| | - Kaixiang Peng
- Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China.
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7
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Zhu QX, Luo Y, He YL. Novel Distributed Alarm Visual Analysis Using Multicorrelation Block-Based PLS and Its Application to Online Root Cause Analysis. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b02963] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yi Luo
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
| | - Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
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8
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Rong M, Shi H, Tan S. Large-Scale Supervised Process Monitoring Based on Distributed Modified Principal Component Regression. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b02163] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mengyu Rong
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Hongbo Shi
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Shuai Tan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
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9
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Rong M, Shi H, Wang F, Tan S. Distributed process monitoring framework based on decomposed modified partial least squares. CAN J CHEM ENG 2019. [DOI: 10.1002/cjce.23559] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Mengyu Rong
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology Shanghai 200237 P. R. China
| | - Hongbo Shi
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology Shanghai 200237 P. R. China
| | - Fan Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology Shanghai 200237 P. R. China
| | - Shuai Tan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology Shanghai 200237 P. R. China
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10
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Jiang Q, Yan X, Huang B. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b02391] [Citation(s) in RCA: 158] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Qingchao Jiang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Xuefeng Yan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Biao Huang
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G2V4, Canada
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11
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Huang J, Ersoy OK, Yan X. Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description. ISA TRANSACTIONS 2019; 85:119-128. [PMID: 30389247 DOI: 10.1016/j.isatra.2018.10.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 08/15/2018] [Accepted: 10/08/2018] [Indexed: 06/08/2023]
Abstract
This study describes a dynamic large-scale process fault detection algorithm based on multi-block slow feature analysis by taking advantages of both multi-block algorithms in highlighting the local information and slow feature analysis in extracting the different dynamics of process data. A mutual information-based relevance matrix is first calculated to measure the correlation between any two variables, and then K-means clustering is used to cluster the original variables into several blocks by gathering the variables with similar relevance vectors into the same block. Slow feature analysis is applied in each block. A support vector data description is utilized to give a final decision. The proposed algorithm is tested with a well-known Tennessee Eastman (TE) process. The fault detection results show the efficiency and the superiority of the proposed method as compared to other related methods.
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Affiliation(s)
- Jian Huang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Okan K Ersoy
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
| | - Xuefeng Yan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
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12
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Deng X, Deng J. Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b04794] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xiaogang Deng
- College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
| | - Jiawei Deng
- College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
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13
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Li W, Zhao C, Huang B. Distributed Dynamic Modeling and Monitoring for Large-Scale Industrial Processes under Closed-Loop Control. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b02683] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Wenqing Li
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Chunhui Zhao
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
| | - Biao Huang
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada
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14
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Das L, Kumar G, Rengaswamy R, Srinivasan B. A novel approach for benchmarking and assessing the performance of state estimators. ISA TRANSACTIONS 2018; 80:137-145. [PMID: 29958650 DOI: 10.1016/j.isatra.2018.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 05/27/2018] [Accepted: 06/11/2018] [Indexed: 06/08/2023]
Abstract
State estimation is a widely adopted soft sensing technique that incorporates predictions from an accurate model of the process and measurements to provide reliable estimates of unmeasured variables. The reliability of such estimators is threatened by measurement related challenges and model inaccuracies. In this article, a method for benchmarking of state estimation techniques is proposed. This method can be used to quantify the performance and hence reliability of an estimator. The Hurst exponents of a posteriori filtering errors are analyzed to characterize a benchmark (minimum mean squared error) estimator, similar to the minimum variance control benchmark developed for control loops. A distance metric is then used to quantify the extent of deviation of an estimator from the benchmark. The proposed technique is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables. Simulation studies are carried out with Kalman based as well as Monte Carlo based estimators whose computational details are significantly different. Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator. The strengths and limitations of the proposed technique are discussed with guidelines on applications and deployment of the technique in a real life system.
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Affiliation(s)
- Laya Das
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India
| | - Gaurav Kumar
- Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India
| | - Raghunathan Rengaswamy
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India
| | - Babji Srinivasan
- Department of Chemical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, 382355, Gujarat, India.
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15
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Khatib S, Daoutidis P, Almansoori A. System Decomposition for Distributed Multivariate Statistical Process Monitoring by Performance Driven Agglomerative Clustering. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b01708] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shaaz Khatib
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Ali Almansoori
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
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16
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Wang L, Deng X. Multi-block principal component analysis based on variable weight information and its application to multivariate process monitoring. CAN J CHEM ENG 2017. [DOI: 10.1002/cjce.23037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Lei Wang
- College of Information and Control Engineering; China University of Petroleum; Qingdao 266580 China
| | - Xiaogang Deng
- College of Information and Control Engineering; China University of Petroleum; Qingdao 266580 China
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17
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Wang B, Yan X, Jin Y. Fault detection based on polygon area statistics of transformation matrix identified from combined moving window data. KOREAN J CHEM ENG 2016. [DOI: 10.1007/s11814-016-0201-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Lv Z, Yan X. Hierarchical Support Vector Data Description for Batch Process Monitoring. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.6b00901] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Zhaomin Lv
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Xuefeng Yan
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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19
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Huang J, Yan X. Angle-Based Multiblock Independent Component Analysis Method with a New Block Dissimilarity Statistic for Non-Gaussian Process Monitoring. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.6b00093] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jian Huang
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Xuefeng Yan
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
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20
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Gao H, Xu Y, Zhu Q. Spatial Interpretive Structural Model Identification and AHP-Based Multimodule Fusion for Alarm Root-Cause Diagnosis in Chemical Processes. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.5b04268] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Huihui Gao
- Engineering
Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
- College
of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yuan Xu
- Engineering
Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
- College
of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Qunxiong Zhu
- Engineering
Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
- College
of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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21
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Li N, Yan W, Yang Y. Spatial-Statistical Local Approach for Improved Manifold-Based Process Monitoring. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b00257] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Nan Li
- Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Weiwu Yan
- Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Yupu Yang
- Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
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22
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23
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Jiang Q, Wang B, Yan X. Multiblock Independent Component Analysis Integrated with Hellinger Distance and Bayesian Inference for Non-Gaussian Plant-Wide Process Monitoring. Ind Eng Chem Res 2015. [DOI: 10.1021/ie403540b] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Qingchao Jiang
- Key Laboratory of Advanced Control
and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Bei Wang
- Key Laboratory of Advanced Control
and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Xuefeng Yan
- Key Laboratory of Advanced Control
and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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24
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Wang B, Yan X, Jiang Q. Loading-Based Principal Component Selection for PCA Integrated with Support Vector Data Description. Ind Eng Chem Res 2015. [DOI: 10.1021/ie503618r] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bei Wang
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Xuefeng Yan
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Qingchao Jiang
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
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25
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Huang J, Yan X. Gaussian and non-Gaussian Double Subspace Statistical Process Monitoring Based on Principal Component Analysis and Independent Component Analysis. Ind Eng Chem Res 2015. [DOI: 10.1021/ie5025358] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Jian Huang
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Xuefeng Yan
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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26
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Song B, Shi H, Ma Y, Wang J. Multisubspace Principal Component Analysis with Local Outlier Factor for Multimode Process Monitoring. Ind Eng Chem Res 2014. [DOI: 10.1021/ie502344q] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Bing Song
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Hongbo Shi
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Yuxin Ma
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Jianping Wang
- Key Laboratory of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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27
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Fault detection and identification using a Kullback-Leibler divergence based multi-block principal component analysis and bayesian inference. KOREAN J CHEM ENG 2014. [DOI: 10.1007/s11814-013-0295-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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Zhaomin L, Qingchao J, Xuefeng Y. Batch Process Monitoring Based on Multisubspace Multiway Principal Component Analysis and Time-Series Bayesian Inference. Ind Eng Chem Res 2014. [DOI: 10.1021/ie403576c] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lv Zhaomin
- Key Laboratory
of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Jiang Qingchao
- Key Laboratory
of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Yan Xuefeng
- Key Laboratory
of Advanced
Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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