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Process diagnosis based on qualitative trend similarities using a sequence matching algorithm

This paper focuses on process diagnosis based on symptoms described by qualitative trends extracted from signals. Main contributions are a new similarity algorithm between qualitative sequences and a generalised approach for on-line diagnosis based on that similarity measure. Process situations are identified by converting sensor time series into qualitative sequences and comparing them with those corresponding to known faulty states. Performance of this approach has been evaluated on line in a steam generator plant

This work has been supported by the research project DPI2009-07891 funded by the Spanish Government

Elsevier

Manager: Ministerio de Ciencia e Innovación (Espanya)
Author: Gamero Argüello, Fco. Ignacio
Meléndez i Frigola, Joaquim
Colomer Llinàs, Joan
Abstract: This paper focuses on process diagnosis based on symptoms described by qualitative trends extracted from signals. Main contributions are a new similarity algorithm between qualitative sequences and a generalised approach for on-line diagnosis based on that similarity measure. Process situations are identified by converting sensor time series into qualitative sequences and comparing them with those corresponding to known faulty states. Performance of this approach has been evaluated on line in a steam generator plant
This work has been supported by the research project DPI2009-07891 funded by the Spanish Government
Document access: http://hdl.handle.net/2072/299565
Language: eng
Publisher: Elsevier
Rights: Tots els drets reservats
Subject: Control automàtic
Automatic control
Title: Process diagnosis based on qualitative trend similarities using a sequence matching algorithm
Type: info:eu-repo/semantics/article
Repository: Recercat

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