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Wang J, Wei M, Xing X. Static gain estimation for nonlinear dynamic systems from steady-state values hidden in historical data. ISA TRANSACTIONS 2022; 120:78-88. [PMID: 33745693 DOI: 10.1016/j.isatra.2021.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
Static gains are often required for control, diagnosis and optimization of nonlinear dynamic systems. This paper proposes a new approach to estimate static gains for nonlinear dynamic systems from steady-state values hidden in historical data. First, steady-state values of system inputs and outputs are extracted by automatically finding data segments in steady-state conditions. Second, static gains of nonlinear dynamic systems in different operating conditions are estimated via linear regression from these steady-state values. The proposed approach has two practical features: (i) estimated static gains can be verified in a convincing way, because the validness of extracted steady-state values is confirmed by visualizing data segments in steady-state conditions, and the accuracy of estimated static gains is verified by comparing the extracted steady-state values and their estimates; (ii) the proposed approach is simple to understand and implement in practice, since it only involves a linear equation between steady-state values and static gains, as well as a basic technique of linear regression. Numerical simulation and industrial application demonstrate the effectiveness of the proposed approach.
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Affiliation(s)
- Jiandong Wang
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong Province, China.
| | - Mengyao Wei
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong Province, China.
| | - Xiaotong Xing
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong Province, China.
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2
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Mendes PSF, Siradze S, Pirro L, Thybaut JW. Extracting kinetic information in catalysis: an automated tool for the exploration of small data. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00215e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Kinetically relevant information for heterogeneously catalysed reactions is automatically extracted from small datasets by means of a newly-developed machine learning chemically-enriched tool.
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Affiliation(s)
- Pedro S. F. Mendes
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Sébastien Siradze
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Laura Pirro
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
| | - Joris W. Thybaut
- Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Technologiepark 125, 9052 Ghent, Belgium
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Yao Y, Dai Y, Luo W. Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis. SENSORS 2021; 21:s21238075. [PMID: 34884082 PMCID: PMC8662448 DOI: 10.3390/s21238075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 11/23/2021] [Accepted: 11/30/2021] [Indexed: 11/30/2022]
Abstract
The products of a batch process have high economic value. Meanwhile, a batch process involves complex chemicals and equipment. The variability of its operation leads to a high failure rate. Therefore, early fault diagnosis of batch processes is of great significance. Usually, the available information of the sensor data in batch processing is obscured by its noise. The multistage variation of data results in poor diagnostic performance. This paper constructed a standardized method to enlarge fault information as well as a batch fault diagnosis method based on trend analysis. First, an adaptive standardization based on the time window was created; second, utilizing quadratic fitting, we extracted a data trend under the window; third, a new trend recognition method based on the Euclidean distance calculation principle was composed. The method was verified in penicillin fermentation. We constructed two test datasets: one based on an existing batch, and one based on an unknown batch. The average diagnostic rate of each group was 100% and 87.5%; the mean diagnosis time was the same; 0.2083 h. Compared with traditional fault diagnosis methods, this algorithm has better fault diagnosis ability and feature extraction ability.
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Affiliation(s)
- Yuman Yao
- College of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China; (Y.Y.); (W.L.)
| | - Yiyang Dai
- School of Chemical Engineering, Sichuan University, Chengdu 610065, China
- Correspondence:
| | - Wenjia Luo
- College of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China; (Y.Y.); (W.L.)
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Xu Y, Fan C, Zhu QX, Rajabifard A, Chen N, Chen Y, He YL. Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b05403] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yuan Xu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, P. R. China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, P. R. China
- Center for SDI and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Cuihuan Fan
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, P. R. China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, P. R. China
| | - Qun-Xiong Zhu
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, P. R. China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, P. R. China
| | - Abbas Rajabifard
- Center for SDI and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Nengcheng Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, Hubei 430079, P. R. China
| | - Yiqun Chen
- Center for SDI and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Yan-Lin He
- College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, P. R. China
- Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, P. R. China
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5
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Determining the number of segments for piece-wise linear representation of discrete-time signals. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2018.08.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Chen K, Wang J. Normal and Abnormal Data Segmentation Based on Variational Directions and Clustering Algorithms. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b01868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Kuang Chen
- College
of Engineering, Peking University, Beijing, China
| | - Jiandong Wang
- College
of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China
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