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An easy to use GUI ...
An easy to use GUI for simulating big data using Tennessee Eastman process
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- Andersen, Emil B. (författare)
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
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- Udugama, Isuru A. (författare)
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
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- Gernaey, Krist V. (författare)
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
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- Khan, Abdul R. (författare)
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
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- Bayer, Christoph (författare)
- Department of Process Engineering, TH Nuernberg, Nuernberg, Germany
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- Kulahci, Murat (författare)
- Luleå tekniska universitet,Industriell ekonomi,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
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(creator_code:org_t)
- 2021-09
- 2022
- Engelska.
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Ingår i: Quality and Reliability Engineering International. - : John Wiley & Sons. - 0748-8017 .- 1099-1638. ; 38:1, s. 264-282
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Data-driven process monitoring and control techniques and their application to industrial chemical processes are gaining popularity due to the current focus on Industry 4.0, digitalization and the Internet of Things. However, for the development of such techniques, there are significant barriers that must be overcome in obtaining sufficiently large and reliable datasets. As a result, the use of real plant and process data in developing and testing data-driven process monitoring and control tools can be difficult without investing significant efforts in acquiring, treating, and interpreting the data. Therefore, researchers need a tool that effortlessly generates large amounts of realistic and reliable process data without the requirement for additional data treatment or interpretation. In this work, we propose a data generation platform based on the Tennessee Eastman Process simulation benchmark. A graphical user interface (GUI) developed in MATLAB Simulink is presented that enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuous time-dependent processes. An R-Shiny app that interacts with the data generation tool is also presented for illustration purposes. The app can visualize the results generated by the Tennessee Eastman Process and can carry out a standard fault detection and diagnosis studies based on PCA. The data generator GUI is available free of charge for research purposes at https://github.com/dtuprodana/TEP.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)
Nyckelord
- chemical process
- digitalization
- industry 4.0
- process monitoring and control
- process simulator
- process surveillance
- Kvalitetsteknik och logistik
- Quality technology and logistics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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