Fault detection and diagnosis in a heat exchanger

Juan C. Tudon Martinez; Ruben Morales-Menendez; Luis E. Garza Castañon

Fault detection and diagnosis in a heat exchanger

Juan C. Tudon Martinez, Ruben Morales-Menendez, Luis E. Garza Castañon

Resultado de la investigación

Resumen

A comparison between the Dynamic Principal Component Analysis (DPCA) method and a bank of Diagnostic Observers (DO) under the same experimental data from a shell and tube industrial heat exchanger is presented. The comparative analysis shows the performance of both methods when sensors and/or actuators fail. Different metrics are discussed (i.e. robustness, quick detection, isolability capacity, explanation facility, false alarm rates and multiple faults identifiability). DO showed quicker detection for sensor and actuator faults with lower false alarm rate. Also, DO can isolate multiple faults. DPCA required a minor training effort; however, it cannot identify two or more sequential faults.

Idioma original	English
Páginas	265-270
Número de páginas	6
Estado	Published - 1 dic 2009
Publicado de forma externa	Sí
Evento	ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics, Proceedings - Duración: 1 dic 2009 → …

Conference

Conference	ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics, Proceedings
Período	1/12/09 → …

Acceder al documento

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Citar esto

Tudon Martinez, J. C., Morales-Menendez, R., & Garza Castañon, L. E. (2009). Fault detection and diagnosis in a heat exchanger. 265-270. Papel presentado en ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics, Proceedings, . https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=74549171450&origin=inward

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AB - A comparison between the Dynamic Principal Component Analysis (DPCA) method and a bank of Diagnostic Observers (DO) under the same experimental data from a shell and tube industrial heat exchanger is presented. The comparative analysis shows the performance of both methods when sensors and/or actuators fail. Different metrics are discussed (i.e. robustness, quick detection, isolability capacity, explanation facility, false alarm rates and multiple faults identifiability). DO showed quicker detection for sensor and actuator faults with lower false alarm rate. Also, DO can isolate multiple faults. DPCA required a minor training effort; however, it cannot identify two or more sequential faults.

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