| 1. |
- Ahnlund, Jonas, et al.
(author)
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Intelligent reduction of nuisance alarms in process control
- 2003
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In: Notes SAIS/SSLS Joint Workshop.
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Conference paper (peer-reviewed)abstract
- The proper handling of alarms is crucial to automated process control. Fault Detection and Isolation techniques have been widely researched to determine such alarms. In practice, many alarms are only distractive and do not represent a fault situation. Here a toolbox, that uses an expert system called LARA, is proposed that aims to reduce the occurrence of so-called nuisance alarms. It is based on a number of signal classifiers and can be easily extended with metrical abnormality predictors. In a first prototype, a significant reduction of the alarms in a biofueled District Heating Plant has been achieved.
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| 2. |
- Bergquist, Tord, et al.
(author)
-
Signal processing and alarm handling in process control
- 2003
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In: Proceedings of the 16th European Conference on Circuit Theory and Design, ECCTD'03. - 8388309951 ; , s. 72-369
-
Conference paper (peer-reviewed)abstract
- The proper handling of alarms is crucial to automated process control. In practice, many alarms are only distractive and do not represent a fault situation. Here a toolbox, that uses an expert system called LARA, is proposed that aims to reduce the occurrence of so-called nuisance alarms. It is based on a signal classification scheme to propose suitable alarm handling algorithms according to the signals behavior. In a first prototype, a significant reduction of the alarms in a biofueled District Heating Plant has been achieved
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| 3. |
- Bergquist, Tord, et al.
(author)
-
Using correlation in MFM model design and validation
- 2004
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In: Proceedings AILS-04 Workshop. - 1650-1276.
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Conference paper (peer-reviewed)abstract
- Industrial process signals do affect one other. However, causal dependencies, or corre-lation, and thereby consequences of control actions cannot always be understood by the operator, which may lead to accidents. Models explain dependencies and are mostly built on process knowledge and blueprints, while causalities are visible in the measurements and ought to be used to increase process under-standing. In this paper we will discuss why the cross-correlation technique is insufficient for causality detection in industrial processes and we will present a new method that, for instance, can be used to validate the design of multilevel flow models. Keywords: Correlation, multilevel flow model, fault detection, alarm analysis.
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