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Huang J, Sun X, Yang X, Peng K. Fault detection for chemical processes based on non-stationarity sensitive cointegration analysis. ISA TRANSACTIONS 2022; 129:321-333. [PMID: 35190195 DOI: 10.1016/j.isatra.2022.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/21/2021] [Accepted: 02/05/2022] [Indexed: 06/14/2023]
Abstract
Due to the time-varying operation conditions, chemical processes are characterized by non-stationary characteristics, which makes it a great challenge for conventional process monitoring methods to capture the non-stationary variations In the non-stationary processes, the abnormality would cause the stationary variables to be non-stationary. In this article, a non-stationarity sensitive cointegration analysis monitoring method is proposed to explore potential non-stationary variations. First, the essential non-stationary variables are distinguished using Augmented Dickey-Fuller test to eliminate the influence of essential non-stationary under normal conditions. Then by further analyzing the faulty data, the variables which are sensitive to the non-stationary variations are selected. On this basis, cointegration analysis models are established for both the essential non-stationary variables and non-stationarity sensitive variables to explore long-term dynamic equilibrium relationship, respectively. With the selection of non-stationarity sensitive variables, the potential faulty information is emphasized in the process monitoring model, which makes the model capable to handle the non-stationary variations. Finally, the monitoring results are combined through Bayesian inference criterion. The proposed method is applied on the Tennessee Eastman process and a vinyl acetate monomer plant model, and the feasibility and performance are demonstrated.
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Affiliation(s)
- Jian Huang
- 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, China
| | - Xiaoyang Sun
- 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, China
| | - Xu Yang
- 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, 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, China
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Mou M, Zhao X. Incipient fault detection and diagnosis of nonlinear industrial process with missing data. J Taiwan Inst Chem Eng 2022. [DOI: 10.1016/j.jtice.2021.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Abstract
The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.
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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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Li Z, Yan X. Performance-driven ensemble ICA chemical process monitoring based on fault-relevant models. Soft comput 2020. [DOI: 10.1007/s00500-020-04673-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Li Z, Yan X. Complex dynamic process monitoring method based on slow feature analysis model of multi-subspace partitioning. ISA TRANSACTIONS 2019; 95:68-81. [PMID: 31151751 DOI: 10.1016/j.isatra.2019.05.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/14/2019] [Accepted: 05/15/2019] [Indexed: 06/09/2023]
Abstract
This study presents an ensemble monitoring strategy based on slow feature analysis (SFA) model of multi-subspace partitioning for dynamic large-scale process. SFA can effectively extract the various dynamics of process data, where the relationship between process data and slow features (SFs) can be revealed by transformation matrix. The similar projecting directions represent similar importance of variables, and corresponding latent variables (LVs) will show similar monitoring behavior. Several LV subspaces are obtained by dividing the transformation vectors with higher similarity into the same sub-block automatically based on the defined process variable related index and hierarchical clustering, which can avoid the problems of information loss and the selection of SFs. Then, the S2 statistics constructed in each subspaces are integrated by support vector data description to show an intuitive detection results. Experiments on Tennessee Eastman benchmark process and wastewater treatment process have validated the proposed strategy's effectiveness and excellence.
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Affiliation(s)
- Zhichao Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR 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, PR China.
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Lan T, Tong C, Yu H, Shi X. Statistical monitoring for non-Gaussian processes based on MICA-KDR method. ISA TRANSACTIONS 2019; 94:164-173. [PMID: 31078289 DOI: 10.1016/j.isatra.2019.03.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 10/12/2018] [Accepted: 03/22/2019] [Indexed: 06/09/2023]
Abstract
The focus of the current work attempts to propose a purely data-based model for generating residuals for non-Gaussian process monitoring purposes, the idea of residual generation is borrowed from the field of model-based fault detection and applied in statistical monitoring, the generated residual instead of the measured variables is thus modeled and monitored. The proposed approach first employs the modified independent component analysis (MICA) algorithm to extract independent components (ICs) from a given dataset. Secondly, through assuming but only one variable is missing at one time, the known data regression (KDR) method dealing with missing data problem is then used for estimating the corresponding ICs. The inconsistency between the actual and estimated ICs is called residual and may present much lower level of non-Gaussianity, in contrast to the actual ICs. Thirdly, a principal component analysis based statistical monitoring model can be utilized for online fault detection based on the generated residual. Finally, the superiority and efficiency of the MICA-KDR approach over its counterparts are validated by implementing comparisons on two industrial processes, the proposed MICA-KDR method is demonstrated to be a comparative alternative in monitoring non-Gaussian processes.
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Affiliation(s)
- Ting Lan
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, PR China
| | - Chudong Tong
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, PR China.
| | - Haizhen Yu
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, PR China
| | - Xuhua Shi
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, PR China
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Tao Y, Shi H, Song B, Tan S. Distributed Supervised Fault Detection and Diagnosis for a Non-Gaussian Process. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b00005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yang Tao
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the 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 the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Bing Song
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the 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 the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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Yang C, Zhou L, Huang K, Ji H, Long C, Chen X, Xie Y. Multimode process monitoring based on robust dictionary learning with application to aluminium electrolysis process. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.12.024] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Tong C, Lan T, Zhu Y, Shi X, Chen Y. A missing variable approach for decentralized statistical process monitoring. ISA TRANSACTIONS 2018; 81:8-17. [PMID: 30262178 DOI: 10.1016/j.isatra.2018.07.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 04/12/2018] [Accepted: 07/22/2018] [Indexed: 06/08/2023]
Abstract
The main focus of the current work is on the investigation and application of a missing variable approach in principal component analysis (PCA) model for decentralized process monitoring purpose. Given that the widely studied PCA algorithm can recover the correlations between measured variables, a missing variable approach is employed for computing score estimation error and residual estimation error from the developed PCA model. Through assuming but only one variable is missing in sequence, the residual between the actual and estimated components is generated and then monitored instead of the original data. The presented method implements a missing variable based offline modeling and online monitoring in a decentralized manner. Generally, the generated residual is expected to follow or at least become much closer to a Gaussian distribution, the resulted model has no restriction on Gaussian distributed dataset and can achieve salient monitoring performance in contrast to its counterparts. Finally, its superiority and effectiveness have been demonstrated by conducting comparisons on two industrial examples.
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Affiliation(s)
- Chudong Tong
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo, 315211, PR China; Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology), Ministry of Education, PR China.
| | - Ting Lan
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo, 315211, PR China
| | - Ying Zhu
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo, 315211, PR China
| | - Xuhua Shi
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo, 315211, PR China
| | - Yuwei Chen
- Key Laboratory of Rubber-Plastics, Ministry of Education/Shandong Provincial Key Laboratory of Rubber-Plastics, Qingdao University of Science & Technology, Qingdao, 266042, PR China
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11
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Li Z, Yan X. Adaptive Selective Ensemble-Independent Component Analysis Models for Process Monitoring. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b00591] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhichao Li
- 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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