Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes:
Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
2000
|
| Schriftenreihe: | Advances in Industrial Control
|
| Schlagworte: | |
| Online-Zugang: | UBT01 URL des Erstveröffentlichers |
| Zusammenfassung: | Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis. The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application |
| Beschreibung: | 1 Online-Ressource (XIII, 192 p. 41 illus) |
| ISBN: | 9781447104094 |
| DOI: | 10.1007/978-1-4471-0409-4 |
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| author | Russell, Evan L. Chiang, Leo H. Braatz, Richard D. |
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| discipline | Chemie / Pharmazie |
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| indexdate | 2024-07-10T08:10:06Z |
| institution | BVB |
| isbn | 9781447104094 |
| language | English |
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| spelling | Russell, Evan L. Verfasser aut Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes by Evan L. Russell, Leo H. Chiang, Richard D. Braatz London Springer London 2000 1 Online-Ressource (XIII, 192 p. 41 illus) txt rdacontent c rdamedia cr rdacarrier Advances in Industrial Control Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis. The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application Chemistry Industrial Chemistry/Chemical Engineering Database Management Data Structures Control, Robotics, Mechatronics Chemical engineering Data structures (Computer science) Database management Control engineering Robotics Mechatronics Chemische Verfahrenstechnik (DE-588)4069941-9 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Prozessüberwachung (DE-588)4133922-8 gnd rswk-swf Diagnosesystem (DE-588)4149458-1 gnd rswk-swf Chemische Verfahrenstechnik (DE-588)4069941-9 s Prozessüberwachung (DE-588)4133922-8 s Diagnosesystem (DE-588)4149458-1 s Multivariate Analyse (DE-588)4040708-1 s 1\p DE-604 Chiang, Leo H. aut Braatz, Richard D. aut Erscheint auch als Druck-Ausgabe 9781447111337 https://doi.org/10.1007/978-1-4471-0409-4 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Russell, Evan L. Chiang, Leo H. Braatz, Richard D. Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes Chemistry Industrial Chemistry/Chemical Engineering Database Management Data Structures Control, Robotics, Mechatronics Chemical engineering Data structures (Computer science) Database management Control engineering Robotics Mechatronics Chemische Verfahrenstechnik (DE-588)4069941-9 gnd Multivariate Analyse (DE-588)4040708-1 gnd Prozessüberwachung (DE-588)4133922-8 gnd Diagnosesystem (DE-588)4149458-1 gnd |
| subject_GND | (DE-588)4069941-9 (DE-588)4040708-1 (DE-588)4133922-8 (DE-588)4149458-1 |
| title | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes |
| title_auth | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes |
| title_exact_search | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes |
| title_full | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes by Evan L. Russell, Leo H. Chiang, Richard D. Braatz |
| title_fullStr | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes by Evan L. Russell, Leo H. Chiang, Richard D. Braatz |
| title_full_unstemmed | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes by Evan L. Russell, Leo H. Chiang, Richard D. Braatz |
| title_short | Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes |
| title_sort | data driven methods for fault detection and diagnosis in chemical processes |
| topic | Chemistry Industrial Chemistry/Chemical Engineering Database Management Data Structures Control, Robotics, Mechatronics Chemical engineering Data structures (Computer science) Database management Control engineering Robotics Mechatronics Chemische Verfahrenstechnik (DE-588)4069941-9 gnd Multivariate Analyse (DE-588)4040708-1 gnd Prozessüberwachung (DE-588)4133922-8 gnd Diagnosesystem (DE-588)4149458-1 gnd |
| topic_facet | Chemistry Industrial Chemistry/Chemical Engineering Database Management Data Structures Control, Robotics, Mechatronics Chemical engineering Data structures (Computer science) Database management Control engineering Robotics Mechatronics Chemische Verfahrenstechnik Multivariate Analyse Prozessüberwachung Diagnosesystem |
| url | https://doi.org/10.1007/978-1-4471-0409-4 |
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