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)
| Summary: | 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. |
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