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Huang J, Yang X, Shardt YA, Yan X. Sparse modeling and monitoring for industrial processes using sparse, distributed principal component analysis. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.04.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Application of graph theory and filter based variable selection methods in the design of a distributed data-driven monitoring system. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Cui Q, Li S. Process monitoring method based on correlation variable classification and vine copula. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23702] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Qun Cui
- Key Laboratory of Advanced Control and Optimization for Chemical ProcessesMinistry of Education, East China University of Science and Technology Shanghai China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical ProcessesMinistry of Education, East China University of Science and Technology Shanghai China
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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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Khatib S, Daoutidis P. Optimal Feature Selection for Distributed Data-Driven Process Monitoring. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b03872] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shaaz Khatib
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
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Concurrent analysis of variable correlation and data distribution for monitoring large-scale processes under varying operation conditions. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.03.061] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Khatib S, Daoutidis P, Almansoori A. System Decomposition for Distributed Multivariate Statistical Process Monitoring by Performance Driven Agglomerative Clustering. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b01708] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shaaz Khatib
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Ali Almansoori
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
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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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