Non-obtrusive stiction detection methods for control systems
Industrial processes play a key role in the production sector. Production demands have forced the search for strategies such as automatic diagnosis to maintain continuous production with minimized machine failures. An industrial process provides many measured, controlled, and manipulated variables t...
- Autores:
-
Escobar Davidson, Leonardo
Sucerquia Rincones, Stephany
Hadechni Bonett, Samir
Ramírez Parra, Jhon
Coll Velásquez, Jean
Beleño Saenz, Kelvin
Jiménez-Cabas, Javier
Díaz Saenz, Carlos
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8000
- Acceso en línea:
- https://hdl.handle.net/11323/8000
https://repositorio.cuc.edu.co/
- Palabra clave:
- Control systems
Hysteresis
Cross correlation
Non-linearity
Curve fitting
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | Industrial processes play a key role in the production sector. Production demands have forced the search for strategies such as automatic diagnosis to maintain continuous production with minimized machine failures. An industrial process provides many measured, controlled, and manipulated variables that associate nonlinearities and uncertainties, so it is necessary to monitor them, to acquire information about the behavior of the process. Historical and present information resulting from monitoring is used to implement intelligent monitoring systems. Within the monitoring scheme is the detection of failures, diagnosis, and restoration of operating conditions according to process performance criteria [1]. |
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