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Intelligent Industrial Alarm Systems [electronic resource] : Advanced Analysis and Design Methods / by Jiandong Wang, Wenkai Hu, Tongwen Chen.

Por: Wang, Jiandong [author.]Colaborador(es): Hu, Wenkai [author.] | Chen, Tongwen [author.] | SpringerLink (Online service)Tipo de material: Texto

TextoEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XIV, 425 p. 244 illus., 208 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819765164Tema(s): Control engineering | Robotics | Automation | Production engineering | Industrial engineering | Control, Robotics, Automation | Process Engineering | Industrial AutomationFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 629.8 Clasificación LoC:TJ212-225TJ210.2-211.495Recursos en línea: Libro electrónico Texto

Contenidos:

Overview of Industrial Alarm Systems -- Optimal Design of Univariate Alarm Systems -- Optimal Design of Multivariate Alarm Systems -- Root-Cause Analysis of Alarm Events -- Analysis of Industrial Alarm Floods -- Alarm Visual Analytics and Applications.

En: Springer Nature eBookResumen: This book fills a gap in existing literature by providing a comprehensive academic perspective on industrial alarm systems, offering systematic methodologies, practical techniques, and visual analytic tools for engineers to improve system performance and design. Modern industrial plants rely on computerized monitoring systems to track hundreds of process variables in real time, enabling operators to maintain safe and efficient conditions. Automatic industrial alarm systems play a crucial role in alerting operators to abnormalities, such as high vessel levels, that could lead to unsafe conditions if left unaddressed. While contemporary alarm systems can be plagued with issues like nuisance alarms, recent academic research has introduced advanced methodologies, like Markov chain theory and Bayesian estimation, to optimize alarm parameters and enhance system performance. By integrating these theoretical advancements into practical applications, the goal is to develop intelligent industrial alarm systems that leverage historical data and process knowledge to predict and prevent alarm floods, ultimately ensuring safer and more efficient plant operations.

Existencias ( 1 )
Notas de título ( 3 )

Existencias
Tipo de ítem	Biblioteca actual	Colección	Signatura	Copia número	Estado	Fecha de vencimiento	Código de barras
Libro Electrónico	Biblioteca Electrónica	Colección de Libros Electrónicos		1	No para préstamo

This book fills a gap in existing literature by providing a comprehensive academic perspective on industrial alarm systems, offering systematic methodologies, practical techniques, and visual analytic tools for engineers to improve system performance and design. Modern industrial plants rely on computerized monitoring systems to track hundreds of process variables in real time, enabling operators to maintain safe and efficient conditions. Automatic industrial alarm systems play a crucial role in alerting operators to abnormalities, such as high vessel levels, that could lead to unsafe conditions if left unaddressed. While contemporary alarm systems can be plagued with issues like nuisance alarms, recent academic research has introduced advanced methodologies, like Markov chain theory and Bayesian estimation, to optimize alarm parameters and enhance system performance. By integrating these theoretical advancements into practical applications, the goal is to develop intelligent industrial alarm systems that leverage historical data and process knowledge to predict and prevent alarm floods, ultimately ensuring safer and more efficient plant operations.

UABC ; Perpetuidad