1
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Darányi A, Abonyi J. Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter. SENSORS (BASEL, SWITZERLAND) 2024; 24:719. [PMID: 38339436 PMCID: PMC10857158 DOI: 10.3390/s24030719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024]
Abstract
This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.
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Affiliation(s)
| | - János Abonyi
- HUN-REN-PE Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem u. 10, P.O. Box 158, H-8200 Veszprem, Hungary;
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2
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Prior-knowledge-embedded model predictive control for blood glucose regulation: Towards efficient and safe artificial pancreas. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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3
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Wang J, Swartz CL, Huang K. Data-driven supply chain monitoring using canonical variate analysis. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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4
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Xiong S, Hou Z. Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:7728-7742. [PMID: 34170832 DOI: 10.1109/tnnls.2021.3087481] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used to transform the unknown MAS into an equivalent virtual dynamic linearization data model. Then using the virtual data model, the structure of the distributed model-free adaptive controller is constructed. For the incorrect signal measurements due to the sensor fault, the radial basis function neural network (RBFNN) is first trained for the MAS under the fault-free case, then using the outputs of the well-trained RBFNN and the actual outputs of MAS under sensor fault case, the estimation laws of the unknown fault and system parameters in the virtual data model are designed with only the measured input-output (I/O) data information. Finally, the boundedness of the formation error is analyzed by the contraction mapping method and mathematical induction method. The effectiveness of the proposed algorithm is illustrated by simulation examples.
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5
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Sun X, Cinar A, Yu X, Rashid M, Liu J. Kernel-Regularized Latent-Variable Regression Models for Dynamic Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xiaoyu Sun
- School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, PR China
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Xia Yu
- School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, PR China
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, United States
| | - Jianchang Liu
- School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, PR China
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6
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Li C, Zhou Z, Wen C, Li Z. Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis. ACS OMEGA 2022; 7:6978-6990. [PMID: 35252689 PMCID: PMC8892482 DOI: 10.1021/acsomega.1c06649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/02/2022] [Indexed: 06/14/2023]
Abstract
Independent component analysis (ICA) is an excellent latent variables (LVs) extraction method that can maximize the non-Gaussianity between LVs to extract statistically independent latent variables and which has been widely used in multivariate statistical process monitoring (MSPM). The underlying assumption of ICA is that the observation data are composed of linear combinations of LVs that are statistically independent. However, the assumption is invalid because the observation data are always derived from the nonlinear mixture of LVs due to the nonlinear characteristic in industrial processes. Under this circumstance, the ICA-based fault detection is unable to provide accurate detection for specific faults of industrial processes. Since the observation data come from the nonlinear mixing of LVs, this makes the observation data change faster than the intrinsic LVs on the time scale. The temporal slowness can be regarded as an additional criterion in the extraction of LVs. The slow feature analysis (SFA) derived from the temporal slowness has received extensive attention and application in MSPM in recent years. Simultaneously, the temporal slowness is expected to make up for the problem that the LVs extracted by ICA have difficulty accurately describing the characteristics of the process. To solve the above problems, this work proposes to monitor non-Gaussian and nonlinear processes using the independent slow feature analysis (ISFA) that combines statistical independence and temporal slowness in extracting the LVs. When the observation data are composed of a nonlinear mixture of LVs, the extracted LVs of ISFA can describe the characteristics of the processes better than ICA, thereby improving the accuracy of fault detection for the non-Gaussian and nonlinear processes. The superiority of the proposed method is verified by a numerical example design and the Tennessee-Eastman process.
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Affiliation(s)
- Chang Li
- School
of Engineering, Huzhou University, Huzhou 313000, China
| | - Zhe Zhou
- School
of Engineering, Huzhou University, Huzhou 313000, China
| | - Chenglin Wen
- School
of Automation, Guangdong University of Petrochemical
Technology, Maoming 525000, China
| | - Zuxin Li
- School
of Science and Engineering, Huzhou College, Huzhou 313000, China
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7
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Hassanpour H, Mhaskar P, Risbeck MJ. A hybrid machine learning approach integrating recurrent neural networks with subspace identification for modelling
HVAC
systems. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Hesam Hassanpour
- Department of Chemical Engineering McMaster University Hamilton Ontario Canada
| | - Prashant Mhaskar
- Department of Chemical Engineering McMaster University Hamilton Ontario Canada
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8
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A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.
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9
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Thebelt A, Wiebe J, Kronqvist J, Tsay C, Misener R. Maximizing information from chemical engineering data sets: Applications to machine learning. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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10
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11
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Supervised functional modeling method for long durations of batch processes with limited batch data. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.116991] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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12
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Sadat Lavasani M, Raeisi Ardali N, Sotudeh-Gharebagh R, Zarghami R, Abonyi J, Mostoufi N. Big data analytics opportunities for applications in process engineering. REV CHEM ENG 2021. [DOI: 10.1515/revce-2020-0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.
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Affiliation(s)
- Mitra Sadat Lavasani
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Nahid Raeisi Ardali
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Rahmat Sotudeh-Gharebagh
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Reza Zarghami
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - János Abonyi
- Department of Process Engineering , MTA – PE “Lendület” Complex Systems Monitoring Research Group, University of Pannonia , P.O. Box 158 , Veszprém , Hungary
| | - Navid Mostoufi
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
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13
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14
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Single Fault Diagnosis Method of Sensors in Cascade System Based on Data-Driven. SENSORS 2021; 21:s21217340. [PMID: 34770645 PMCID: PMC8588395 DOI: 10.3390/s21217340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 11/16/2022]
Abstract
The reliability and safety of the cascade system, which is widely applied, have attached attention increasingly. Fault detection and diagnosis can play a significant role in enhancing its reliability and safety. On account of the complexity of the double closed-loop system in operation, the problem of fault diagnosis is relatively complex. For the single fault of the second-order valued system sensors, a real-time fault diagnosis method based on data-driven is proposed in this study. Off-line data is employed to establish static fault detection, location, estimation, and separation models. The static models are calibrated with on-line data to obtain the real-time fault diagnosis models. The real-time calibration, working flow and anti-interference measures of the real-time diagnosis system are given. Experiments results demonstrate the validity and accuracy of the fault diagnosis method, which is suitable for the general cascade system.
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15
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Rahoma A, Imtiaz S, Ahmed S. A new criterion for selection of non‐zero loadings for sparse principal component analysis (
SPCA
). CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.24026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Abdalhamid Rahoma
- The Centre for Risk, Integrity, and Safety Engineering (C‐RISE), Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's Newfoundland Canada
| | - Syed Imtiaz
- The Centre for Risk, Integrity, and Safety Engineering (C‐RISE), Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's Newfoundland Canada
| | - Salim Ahmed
- The Centre for Risk, Integrity, and Safety Engineering (C‐RISE), Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's Newfoundland Canada
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16
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Rezende PHV, Sencio RR, Costa TV. Fault-tolerant control and interval operability for non-square faulty systems. BRAZILIAN JOURNAL OF CHEMICAL ENGINEERING 2021. [DOI: 10.1007/s43153-021-00149-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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17
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Zhang H, Wang F, Li K, Zhao L. Stochastic chance-constrained optimization framework for the thickening-dewatering process with an uncertain feed quantity. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Impact of Process Parameters and Formulation Properties on Dissolution Performance of an Extended Release Tablet: a Multivariate Analysis. J Pharm Innov 2021. [DOI: 10.1007/s12247-021-09570-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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19
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Diagnosis and Monitoring of Volatile Fatty Acids Production from Raw Cheese Whey by Multiscale Time-Series Analysis. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11135803] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Anaerobic treatment is a viable alternative for the treatment of agro-industrial waste. Anaerobic digestion reduces organic load and produces volatile fatty acids (VFA), which are precursors of value-added products such as methane-rich biogas, biohydrogen, and biopolymers. Nowadays, there are no low-cost diagnosis and monitoring systems that analyze the dynamic behavior of key variables in real time, representing a significant limitation for its practical implementation. In this work, the feasibility of using the multiscale analysis to diagnose and monitor the key variables in VFA production by anaerobic treatment of raw cheese whey is presented. First, experiments were carried out to evaluate the performance of the proposed methodology under different operating conditions. Then, experimental pH time series were analyzed using rescaled range (R/S) techniques. Time-series analysis shows that the anaerobic VFA production exhibits a multiscale behavior, identifying three characteristic regions (i.e., three values of Hurst exponent). In addition, the dynamic Hurst exponents show satisfactory correlations with the chemical oxygen demand (COD) consumption and VFA production. The multiscale analysis of pH time series is easy to implement and inexpensive. Hence, it could be used as a diagnosis and indirect monitoring system of key variables in the anaerobic treatment of raw cheese whey.
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20
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Duran‐Villalobos CA, Ogonah O, Melinek B, Bracewell DG, Hallam T, Lennox B. Multivariate statistical data analysis of cell‐free protein synthesis toward monitoring and control. AIChE J 2021. [DOI: 10.1002/aic.17257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Olotu Ogonah
- Department of Biochemical Engineering University College London London UK
| | - Beatrice Melinek
- Department of Biochemical Engineering University College London London UK
| | | | - Trevor Hallam
- Sutro Biopharma, Inc. South San Francisco California USA
| | - Barry Lennox
- Department of Electrical and Electronic Engineering The University of Manchester Manchester UK
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21
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22
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Quantum computing assisted deep learning for fault detection and diagnosis in industrial process systems. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107119] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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23
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Yan L, Peng X, Tong C, Luo L. A Multigroup Fault Detection and Diagnosis Scheme for Multivariate Systems. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03814] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ling Yan
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xin Peng
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Chudong Tong
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Lijia Luo
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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24
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Tang Y, Zhang S. Fault detection of FWTPs in coal‐fired power plants using K‐WD‐KPCA in consideration of multiple operation conditions. ASIA-PAC J CHEM ENG 2020. [DOI: 10.1002/apj.2599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Yuling Tang
- College of Computer Science South‐Central University for Nationalities Wuhan Hubei China
| | - Shirong Zhang
- School of Electrical Engineering and Automation Wuhan University Wuhan Hubei China
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25
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A hybrid modeling approach integrating first-principles knowledge with statistical methods for fault detection in HVAC systems. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107022] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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26
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Kumar A, Zhang Y, Chiu MS. VRFT-based digital controller design using a generalized second-order reference model. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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27
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Wang J, Swartz CLE, Corbett B, Huang K. Supply Chain Monitoring Using Principal Component Analysis. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jing Wang
- School of Computational Science and Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1
| | - Christopher L. E. Swartz
- Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4L7
| | - Brandon Corbett
- ProSensus Inc., 4325 Harvester Road, Unit 12, Burlington, Ontario, Canada, L7L 5M4
| | - Kai Huang
- DeGroote School of Business, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4M4
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28
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Md Nor N, Che Hassan CR, Hussain MA. A review of data-driven fault detection and diagnosis methods: applications in chemical process systems. REV CHEM ENG 2020. [DOI: 10.1515/revce-2017-0069] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractFault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.
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Affiliation(s)
- Norazwan Md Nor
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia
| | - Che Rosmani Che Hassan
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Mohd Azlan Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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29
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Luo L, Wang J, Tong C, Zhu J. Multivariate Fault Detection and Diagnosis Based on Variable Grouping. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c00192] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lijia Luo
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jinpeng Wang
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Chudong Tong
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Junwei Zhu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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30
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31
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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring. Processes (Basel) 2019. [DOI: 10.3390/pr8010024] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.
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32
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Luo L, Xu M, Bao S, Mao J, Tong C. Improvements to the T2 Statistic for Multivariate Fault Detection. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b04112] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [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
| | - Man Xu
- 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
| | - Chudong Tong
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
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33
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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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34
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Moreno M, Liu J, Su Q, Leach C, Giridhar A, Yazdanpanah N, O’Connor T, Nagy ZK, Reklaitis GV. Steady-State Data Reconciliation Framework for a Direct Continuous Tableting Line. J Pharm Innov 2019; 14:221-238. [PMID: 36824482 PMCID: PMC9945915 DOI: 10.1007/s12247-018-9354-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Purpose Reliable process monitoring in real-time remains a challenge for the pharmaceutical industry. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this paper, we present a process model-based framework, which for given sensor network and measurement uncertainties will predict the most likely state of the process. Thus, real-time process decisions, whether for process control or exceptional events management, can be based on the most reliable estimate of the process state. Methods Reliable process monitoring is achieved by using data reconciliation (DR) and gross error detection (GED) to mitigate the effects of random measurement errors and non-random sensor malfunctions. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. We also compare and contrast the model-based DR approach (SSDR-M) to the purely data-driven approach (SSDR-D) based on the use of principal component constructions. Results We report the results of studies on a pilot plant-scale continuous direct compression-based tableting line at steady-state in two subsystems. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. SSDR-M also complies with mass balances and estimate unmeasured variables. Conclusions SSDR successfully estimates the true state of the process in presence of gross errors, as long as steady state is maintained and the redundancy requirement is met. Gross errors are also detected while using SSDR-M or SSDR-D. Process monitoring is more reliable while using the SSDR framework.
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Affiliation(s)
- Mariana Moreno
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Jianfeng Liu
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Qinglin Su
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Cody Leach
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Arun Giridhar
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Nima Yazdanpanah
- Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Thomas O’Connor
- Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
| | - Gintaras V. Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA
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Md Nor N, Hussain MA, Che Hassan CR. Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04438-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Shahnazari H, Mhaskar P, House JM, Salsbury TI. Modeling and fault diagnosis design for HVAC systems using recurrent neural networks. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.04.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Affiliation(s)
- Lijia Luo
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Shiyi Bao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jianfeng Mao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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Rendall R, Chiang LH, Reis MS. Data-driven methods for batch data analysis – A critical overview and mapping on the complexity scale. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.01.014] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zhu QX, Luo Y, He YL. Novel Multiblock Transfer Entropy Based Bayesian Network and Its Application to Root Cause Analysis. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b06392] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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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Luo L. Monitoring Uneven Multistage/Multiphase Batch Processes using Trajectory‐Based Fuzzy Phase Partition and Hybrid MPCA Models. CAN J CHEM ENG 2019. [DOI: 10.1002/cjce.23220] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Lijia Luo
- Institute of Process Equipment and Control EngineeringZhejiang University of TechnologyHangzhouChina
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Hu X, Wang L, Gao F. Genetic-Algorithm-Optimization-Based Infinite Horizon Linear Quadratic Control for Injection Molding Batch Processes with Uncertainty. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b04921] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xiaomin Hu
- School of Science, Hangzhou Dianzi University, Hangzhou 310018, P. R. China
| | - Limin Wang
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, P. R. China
| | - Furong Gao
- Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Kowloon 300071, Hong Kong
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44
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Rashid MM, Patel N, Mhaskar P, Swartz CLE. Handling sensor faults in economic model predictive control of batch processes. AIChE J 2018. [DOI: 10.1002/aic.16460] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Mudassir M. Rashid
- Dept. of Chemical EngineeringMcMaster University Hamilton ON, L8S 4L7 Canada
| | - Nikesh Patel
- Dept. of Chemical EngineeringMcMaster University Hamilton ON, L8S 4L7 Canada
| | - Prashant Mhaskar
- Dept. of Chemical EngineeringMcMaster University Hamilton ON, L8S 4L7 Canada
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Wang J, Qiu K, Liu W, Yu T, Zhao L. Unsupervised-Multiscale-Sequential-Partitioning and Multiple-SVDD-Model-Based Process-Monitoring Method for Multiphase Batch Processes. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b02486] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jianlin Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Kepeng Qiu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Weimin Liu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tao Yu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Liqiang Zhao
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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46
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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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Geng Z, Wang Z, Hu H, Han Y, Lin X, Zhong Y. A fault detection method based on horizontal visibility graph‐integrated complex networks: Application to complex chemical processes. CAN J CHEM ENG 2018. [DOI: 10.1002/cjce.23319] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Zhiqiang Geng
- College of Information Science & TechnologyBeijing University of Chemical TechnologyBeijing 100029China
- Engineering Research Center of Intelligent PSEMinistry of Education in ChinaBeijing 100029China
| | - Zun Wang
- College of Information Science & TechnologyBeijing University of Chemical TechnologyBeijing 100029China
- Engineering Research Center of Intelligent PSEMinistry of Education in ChinaBeijing 100029China
| | - Haixia Hu
- College of Information Science & TechnologyBeijing University of Chemical TechnologyBeijing 100029China
- Engineering Research Center of Intelligent PSEMinistry of Education in ChinaBeijing 100029China
| | - Yongming Han
- College of Information Science & TechnologyBeijing University of Chemical TechnologyBeijing 100029China
- Engineering Research Center of Intelligent PSEMinistry of Education in ChinaBeijing 100029China
| | - Xiaoyong Lin
- College of Information Science & TechnologyBeijing University of Chemical TechnologyBeijing 100029China
- Engineering Research Center of Intelligent PSEMinistry of Education in ChinaBeijing 100029China
| | - Yanhua Zhong
- Jiangmen PolytechnicJiangmenGuangdong 529020China
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49
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Feature learning and process monitoring of injection molding using convolution-deconvolution auto encoders. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.07.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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