1
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Wang Y, Liang J, Ling D, Gu X, Li S. The chemical process monitoring method based on temporal extended orthogonal neighbourhood preserving embedding (TONPE). CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yan Wang
- School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China
| | - Jie Liang
- School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China
| | - Dan Ling
- School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China
| | - Xiao‐guang Gu
- Applied Research Department Henan Big Data Center Zhengzhou China
| | - Shang Li
- School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China
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2
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A Review on Data-Driven Process Monitoring Methods: Characterization and Mining of Industrial Data. Processes (Basel) 2022. [DOI: 10.3390/pr10020335] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. The implementation framework of data-driven process monitoring methods is first introduced. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. Finally, the challenges and possible solutions for actual industrial applications are discussed.
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3
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Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process. Processes (Basel) 2022. [DOI: 10.3390/pr10010122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.
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4
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Cui P, Wang X, Yang Y. Nonparametric manifold learning approach for improved process monitoring. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24066] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Ping Cui
- Key Laboratory of Ministry of Education in System Control and Information Processing, Department of Automation Shanghai Jiao Tong University Shanghai China
| | - Xuhong Wang
- Key Laboratory of Ministry of Education in System Control and Information Processing, Department of Automation Shanghai Jiao Tong University Shanghai China
| | - Yupu Yang
- Key Laboratory of Ministry of Education in System Control and Information Processing, Department of Automation Shanghai Jiao Tong University Shanghai China
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5
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Huang K, Wen H, Liu H, Yang C, Gui W. A geometry constrained dictionary learning method for industrial process monitoring. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.08.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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6
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Fault Monitoring of Chemical Process Based on Sliding Window Wavelet DenoisingGLPP. Processes (Basel) 2021. [DOI: 10.3390/pr9010086] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In industrial process fault monitoring, it is very important to collect accurate data, but in the actual process, there are often various noises that are difficult to eliminate in the collected data due to sensor accuracy, measurement errors, or human factors. Existing statistical process monitoring methods often ignore the problem of data noise. To solve this problem, a sliding window wavelet denoising-global local preserving projections (SWWD-GLPP) process monitoring method is proposed. In the offline stage, the wavelet denoising method is used to denoise the offline data, and then, the GLPP method is used for offline modeling, and then, the control limit is obtained by the kernel density estimation method. In the online phase, the sliding window wavelet denoising method is used to denoise the online data in real time. Then, use the model of the GLPP method to find the statistics, compare them with the control limit, judge the fault situation, and finally, use the contribution graph method to determine the variable that caused the fault, so as to diagnose the fault. This article uses a numerical case to illustrate the effectiveness of the algorithm, using the Tennessee Eastman (TE) process to compare the traditional principal component analysis (PCA) and GLPP methods to further prove the effectiveness and superiority of the method.
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7
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Wu P, Lou S, Zhang X, He J, Gao J. Novel Quality-Relevant Process Monitoring based on Dynamic Locally Linear Embedding Concurrent Canonical Correlation Analysis. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03492] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Ping Wu
- Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, 310018, People’s Republic of China
| | - Siwei Lou
- Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, 310018, People’s Republic of China
| | - Xujie Zhang
- Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, 310018, People’s Republic of China
| | - Jiajun He
- Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, 310018, People’s Republic of China
| | - Jinfeng Gao
- Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou, 310018, People’s Republic of China
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8
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Dong J, Zhang C, Peng K. A novel industrial process monitoring method based on improved local tangent space alignment algorithm. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.053] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring. Processes (Basel) 2020. [DOI: 10.3390/pr8091079] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Data-based process monitoring methods have received tremendous attention in recent years, and modern industrial process data often exhibit dynamic and nonlinear characteristics. Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data. Feature extraction based on manifold learning using spatial or temporal neighbors has been widely used in dynamic process monitoring in recent years, but most of them use linear features and do not take into account the complex nonlinearities of industrial processes. Therefore, a fault detection scheme based on temporal-spatial neighborhood enhanced sparse autoencoder is proposed in this paper. Firstly, it selects the temporal neighborhood and spatial neighborhood of the sample at the current time within the time window with a certain length, the spatial similarity and time serial correlation are used for weighted reconstruction, and the reconstruction combines the current sample as the input of the sparse stack autoencoder (SSAE) to extract the correlation features between the current sample and the neighborhood information. Two statistics are constructed for fault detection. Considering that both types of neighborhood information contain spatial-temporal structural features, Bayesian fusion strategy is used to integrate the two parts of the detection results. Finally, the superiority of the method in this paper is illustrated by a numerical example and the Tennessee Eastman process.
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10
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Chen H, Wu J, Jiang B, Chen W. A modified neighborhood preserving embedding-based incipient fault detection with applications to small-scale cyber-physical systems. ISA TRANSACTIONS 2020; 104:175-183. [PMID: 31466727 DOI: 10.1016/j.isatra.2019.08.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 07/05/2019] [Accepted: 08/10/2019] [Indexed: 06/10/2023]
Abstract
Industrial cyber-physical systems (ICPSs) are backbones of the Industrial 4.0 where control, physical entities, and monitoring are intensively interacted. Aiming to improve safety of a small-scale ICPS whose physical entity is an electrical drive system, this paper will develop a new detection strategy for incipient faults in neighborhood preserving embedding (NPE) framework that can provide stable solutions. The proposed modified NPE can not only extract local information effectively on data manifold of the ICPS but also solve the singularity problem caused by generalized eigenvalue decomposition skills. Additional advantages of this design for ICPSs include the enhanced fault detectability, inherent scalability, and accelerated computation efficiency. The proposed method is firstly evaluated by mathematical deviations and then is evaluated by its application to a small-scale ICPS. Three sets of experimental results show the efficacy of the proposed method in dealing with online detection of incipient faults in the ICPS.
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Affiliation(s)
- Hongtian Chen
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 169 Shengtai West Road, Jiang Ning District, Nanjing, 211106, China
| | - Jianping Wu
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 169 Shengtai West Road, Jiang Ning District, Nanjing, 211106, China
| | - Bin Jiang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 169 Shengtai West Road, Jiang Ning District, Nanjing, 211106, China.
| | - Wen Chen
- Division of Engineering Technology, Wayne State University, Detroit, MI, 48202, USA
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11
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Li S, Luo J, Hu Y. Semi-supervised process fault classification based on convolutional ladder network with local and global feature fusion. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106843] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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12
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Gao X, Xu Z, Li Z, Wang P. Batch process monitoring using multiway Laplacian autoencoders. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23738] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Xuejin Gao
- Faculty of Information TechnologyBeijing University of Technology Beijing China
- Engineering Research Centre of Digital CommunityMinistry of Education Beijing China
- Beijing Laboratory for Urban Mass Transit Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
| | - Zidong Xu
- Faculty of Information TechnologyBeijing University of Technology Beijing China
- Engineering Research Centre of Digital CommunityMinistry of Education Beijing China
- Beijing Laboratory for Urban Mass Transit Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
| | - Zheng Li
- Faculty of Information TechnologyBeijing University of Technology Beijing China
- Engineering Research Centre of Digital CommunityMinistry of Education Beijing China
- Beijing Laboratory for Urban Mass Transit Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
| | - Pu Wang
- Faculty of Information TechnologyBeijing University of Technology Beijing China
- Engineering Research Centre of Digital CommunityMinistry of Education Beijing China
- Beijing Laboratory for Urban Mass Transit Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
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13
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Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.075] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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14
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Fu Y, Luo C. Joint Structure Preserving Embedding Model and Its Application for Process Monitoring. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03077] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yuanjian Fu
- College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
| | - Chaomin Luo
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, Mississippi 39762, United States
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15
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Zhao X, Jia M. A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.010] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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16
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Zhao H, Lai Z, Chen Y. Global-and-local-structure-based neural network for fault detection. Neural Netw 2019; 118:43-53. [DOI: 10.1016/j.neunet.2019.05.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 04/13/2019] [Accepted: 05/24/2019] [Indexed: 11/25/2022]
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17
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Zhou Y, Li S, Xiong N. Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b04081] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yang Zhou
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Ning Xiong
- School of Innovation, Design and Engineering, Mälardalen University, SE-72123 Västeras, Sweden
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18
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Cui P, Zhan C, Yang Y. Improved nonlinear process monitoring based on ensemble KPCA with local structure analysis. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2018.12.028] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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19
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20
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Zhao H, Lai Z. Neighborhood preserving neural network for fault detection. Neural Netw 2018; 109:6-18. [PMID: 30388431 DOI: 10.1016/j.neunet.2018.09.010] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 07/24/2018] [Accepted: 09/21/2018] [Indexed: 11/15/2022]
Abstract
A novel statistical feature extraction method, called the neighborhood preserving neural network (NPNN), is proposed in this paper. NPNN can be viewed as a nonlinear data-driven fault detection technique through preserving the local geometrical structure of normal process data. The "local geometrical structure " means that each sample can be constructed as a linear combination of its neighbors. NPNN is characterized by adaptively training a nonlinear neural network which takes the local geometrical structure of the data into consideration. Moreover, in order to extract uncorrelated and faithful features, NPNN adopts orthogonal constraints in the objective function. Through backpropagation and eigen decomposition (ED) technique, NPNN is optimized to extract low-dimensional features from original high-dimensional process data. After nonlinear feature extraction, Hotelling T2 statistic and the squared prediction error (SPE) statistic are utilized for the fault detection tasks. The advantages of the proposed NPNN method are demonstrated by both theoretical analysis and case studies on the Tennessee Eastman (TE) benchmark process. Extensive experimental results show the superiority of NPNN in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of NPNN can be found in https://github.com/htzhaoecust/npnn.
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Affiliation(s)
- Haitao Zhao
- Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, PR China
| | - Zhihui Lai
- Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, PR China.
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21
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Zhao X, Jia M. Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.038] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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22
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Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.02.091] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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23
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Luo L, Bao S, Mao J, Ding Z. Industrial Process Monitoring Based on Knowledge–Data Integrated Sparse Model and Two-Level Deviation Magnitude Plots. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.7b02150] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lijia Luo
- Institute of Process Equipment
and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Shiyi Bao
- Institute of Process Equipment
and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jianfeng Mao
- Institute of Process Equipment
and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zhenyu Ding
- Institute of Process Equipment
and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
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24
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Zhan C, Li S, Yang Y. Enhanced Fault Detection Based on Ensemble Global–Local Preserving Projections with Quantitative Global–Local Structure Analysis. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b01642] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Chengjun Zhan
- Department of Automation and Key Laboratory of System Control and Information
Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China
| | - Shuanghong Li
- Department of Automation and Key Laboratory of System Control and Information
Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China
| | - Yupu Yang
- Department of Automation and Key Laboratory of System Control and Information
Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China
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25
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Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection. Front Chem Sci Eng 2017. [DOI: 10.1007/s11705-017-1675-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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26
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Tong C, Shi X, Lan T. Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis. ISA TRANSACTIONS 2016; 65:407-417. [PMID: 27435000 DOI: 10.1016/j.isatra.2016.06.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Revised: 06/02/2016] [Accepted: 06/30/2016] [Indexed: 06/06/2023]
Abstract
Multivariate statistical methods have been widely applied to develop data-based process monitoring models. Recently, a multi-manifold projections (MMP) algorithm was proposed for modeling and monitoring chemical industrial processes, the MMP is an effective tool for preserving the global and local geometric structure of the original data space in the reduced feature subspace, but it does not provide orthogonal basis functions for data reconstruction. Recognition of this issue, an improved version of MMP algorithm named orthogonal MMP (OMMP) is formulated. Based on the OMMP model, a further processing step and a different monitoring index are proposed to model and monitor the variation in the residual subspace. Additionally, a novel variable contribution analysis is presented for fault diagnosis by integrating the nearest in-control neighbor calculation and reconstruction-based contribution analysis. The validity and superiority of the proposed fault detection and diagnosis strategy are then validated through case studies on the Tennessee Eastman benchmark process.
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Affiliation(s)
- Chudong Tong
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P.R. China.
| | - Xuhua Shi
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P.R. China
| | - Ting Lan
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P.R. China
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27
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Miao A, Li P, Ye L. Locality preserving based data regression and its application for soft sensor modelling. CAN J CHEM ENG 2016. [DOI: 10.1002/cjce.22568] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Aimin Miao
- Department of Electronic Engineering; School of Information; Yunnan University; Kunming, 650091 Yunnan China
| | - Peng Li
- Department of Electronic Engineering; School of Information; Yunnan University; Kunming, 650091 Yunnan China
| | - Lingjian Ye
- Ningbo Institute of Technology; Zhejiang University; Ningbo 315100, Zhejiang China
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28
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Zhong B, Wang J, Zhou J, Wu H, Jin Q. Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.5b02559] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Bin Zhong
- College
of Information Science
and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jing Wang
- College
of Information Science
and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jinglin Zhou
- College
of Information Science
and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Haiyan Wu
- College
of Information Science
and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Qibing Jin
- College
of Information Science
and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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29
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Shao W, Tian X, Wang P. Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor. Chin J Chem Eng 2015. [DOI: 10.1016/j.cjche.2015.11.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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30
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Luo L, Bao S, Mao J, Tang D. Nonlinear Process Monitoring Using Data-Dependent Kernel Global–Local Preserving Projections. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b02266] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Lijia Luo
- College of Mechanical
Engineering, Zhejiang University
of Technology, Engineering Research
Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China
| | - Shiyi Bao
- College of Mechanical
Engineering, Zhejiang University
of Technology, Engineering Research
Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China
| | - Jianfeng Mao
- College of Mechanical
Engineering, Zhejiang University
of Technology, Engineering Research
Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China
| | - Di Tang
- College of Mechanical
Engineering, Zhejiang University
of Technology, Engineering Research
Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China
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31
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Zhang H, Tian X, Deng X, Cai L. A local and global statistics pattern analysis method and its application to process fault identification. Chin J Chem Eng 2015. [DOI: 10.1016/j.cjche.2015.09.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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32
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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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33
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Li S, Zhou X, Shi H, Qiao Z, Zheng Z. Monitoring of Multimode Processes Based on Subspace Decomposition. Ind Eng Chem Res 2015. [DOI: 10.1021/ie504730x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | - Zhi Qiao
- NUS
Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 117456, Republic of Singapore
- Department
of Physics and Centre for Computational Science and Engineering, National University of Singapore, Singapore, 117542, Republic of Singapore
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34
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35
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Noise-resistant joint diagonalization independent component analysis based process fault detection. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.08.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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36
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Li N, Yang Y. Ensemble Kernel Principal Component Analysis for Improved Nonlinear Process Monitoring. Ind Eng Chem Res 2014. [DOI: 10.1021/ie503034j] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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
| | - 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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37
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Affiliation(s)
- Estanislao Musulin
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas, Ocampo y Esmeralda, S2000BTP Rosario, Argentina
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Miao A, Ge Z, Song Z, Zhou L. Time Neighborhood Preserving Embedding Model and Its Application for Fault Detection. Ind Eng Chem Res 2013. [DOI: 10.1021/ie400854f] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Aimin Miao
- State Key
Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Department
of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, P. R.China
| | - Zhiqiang Ge
- State Key
Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Department
of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, P. R.China
| | - Zhihuan Song
- State Key
Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Department
of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, P. R.China
| | - Le Zhou
- State Key
Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Department
of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, P. R.China
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40
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Tong C, Song Y, Yan X. Distributed Statistical Process Monitoring Based on Four-Subspace Construction and Bayesian Inference. Ind Eng Chem Res 2013. [DOI: 10.1021/ie400544q] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Chudong Tong
- 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
| | - Yu 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
| | - 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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DENG X, TIAN X. Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection. Chin J Chem Eng 2013. [DOI: 10.1016/s1004-9541(13)60454-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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