Data-driven Methods for Fault Localization in Process Technology
Control systems at production plants consist of a large number of process variables. When detecting abnormal behavior, these variables generate an alarm. Due to the interconnection of the plant's devices the fault can lead to an alarm flood. This again hides the original location of the causing...
- Autores:
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2013
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/17652
- Acceso en línea:
- https://directory.doabooks.org/handle/20.500.12854/44557
http://hdl.handle.net/20.500.12010/17652
- Palabra clave:
- Time series
Signal processing
Data Mining
Minería de datos
Búsqueda en bases de datos
Búsqueda electrónica de recursos de información
- Rights
- License
- Abierto (Texto Completo)
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| dc.title.spa.fl_str_mv |
Data-driven Methods for Fault Localization in Process Technology |
| title |
Data-driven Methods for Fault Localization in Process Technology |
| spellingShingle |
Data-driven Methods for Fault Localization in Process Technology Time series Signal processing Data Mining Minería de datos Búsqueda en bases de datos Búsqueda electrónica de recursos de información |
| title_short |
Data-driven Methods for Fault Localization in Process Technology |
| title_full |
Data-driven Methods for Fault Localization in Process Technology |
| title_fullStr |
Data-driven Methods for Fault Localization in Process Technology |
| title_full_unstemmed |
Data-driven Methods for Fault Localization in Process Technology |
| title_sort |
Data-driven Methods for Fault Localization in Process Technology |
| dc.subject.spa.fl_str_mv |
Time series Signal processing Data Mining |
| topic |
Time series Signal processing Data Mining Minería de datos Búsqueda en bases de datos Búsqueda electrónica de recursos de información |
| dc.subject.lemb.spa.fl_str_mv |
Minería de datos Búsqueda en bases de datos Búsqueda electrónica de recursos de información |
| description |
Control systems at production plants consist of a large number of process variables. When detecting abnormal behavior, these variables generate an alarm. Due to the interconnection of the plant's devices the fault can lead to an alarm flood. This again hides the original location of the causing device. In this work several data-driven approaches for root cause localization are proposed, compared and combined. All methods analyze disturbed process data for backtracking the propagation path. |
| publishDate |
2013 |
| dc.date.created.none.fl_str_mv |
2013 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-23T15:08:43Z |
| dc.date.available.none.fl_str_mv |
2021-02-23T15:08:43Z |
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http://purl.org/coar/resource_type/c_2f33 |
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http://purl.org/coar/resource_type/c_2f33 |
| dc.identifier.isbn.none.fl_str_mv |
9783731500988 |
| dc.identifier.other.none.fl_str_mv |
https://directory.doabooks.org/handle/20.500.12854/44557 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12010/17652 |
| dc.identifier.doi.none.fl_str_mv |
10.5445/KSP/1000036427 |
| identifier_str_mv |
9783731500988 10.5445/KSP/1000036427 |
| url |
https://directory.doabooks.org/handle/20.500.12854/44557 http://hdl.handle.net/20.500.12010/17652 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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| dc.rights.local.spa.fl_str_mv |
Abierto (Texto Completo) |
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https://creativecommons.org/licenses/by-sa/4.0/ |
| rights_invalid_str_mv |
Abierto (Texto Completo) https://creativecommons.org/licenses/by-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
| dc.format.extent.spa.fl_str_mv |
XVIII, 194 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
KIT Scientific Publishing |
| institution |
Universidad de Bogotá Jorge Tadeo Lozano |
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2021-02-23T15:08:43Z2021-02-23T15:08:43Z20139783731500988https://directory.doabooks.org/handle/20.500.12854/44557http://hdl.handle.net/20.500.12010/1765210.5445/KSP/1000036427Control systems at production plants consist of a large number of process variables. When detecting abnormal behavior, these variables generate an alarm. Due to the interconnection of the plant's devices the fault can lead to an alarm flood. This again hides the original location of the causing device. In this work several data-driven approaches for root cause localization are proposed, compared and combined. All methods analyze disturbed process data for backtracking the propagation path.XVIII, 194 páginasapplication/pdfengKIT Scientific PublishingTime seriesSignal processingData MiningMinería de datosBúsqueda en bases de datosBúsqueda electrónica de recursos de informaciónData-driven Methods for Fault Localization in Process TechnologyAbierto (Texto Completo)https://creativecommons.org/licenses/by-sa/4.0/http://purl.org/coar/access_right/c_abf2http://purl.org/coar/resource_type/c_2f33Kühnert, ChristianTHUMBNAIL978-3-7315-0098-8.pdf.jpg978-3-7315-0098-8.pdf.jpgIM Thumbnailimage/jpeg14301https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17652/3/978-3-7315-0098-8.pdf.jpg51c9660179a1964140ad58700739fb53MD53open accessORIGINAL978-3-7315-0098-8.pdf978-3-7315-0098-8.pdfVer documentoapplication/pdf6441949https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17652/1/978-3-7315-0098-8.pdf5ccdad694a81547eacdc9f9c70b16631MD51open accessLICENSElicense.txtlicense.txttext/plain; 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