| 1. |
- Ahnlund, Jonas, et al.
(author)
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An alarm reduction application at a district heating plant
- 2003
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In: EFTA 2003. 2003 IEEE Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.03TH8696). - 0780379373 ; , s. 187-190
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Conference paper (peer-reviewed)abstract
- The proper handling of alarms is crucial to any automated process control. In practice, many alarms are only distractive and do not represent a fault situation. In an ongoing project a computerized tool is applied, that aims to remove such nuisance alarms, a so-called alarm cleanup. This is a general, systematic approach that takes advantage of the control system's built-in functions, and is a first step to an enhanced overall alarm situation. In a first sharp case study, a significant reduction of the alarms was achieved at a biofueled district heating plant
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| 2. |
- Ahnlund, Jonas, et al.
(author)
-
Intelligent reduction of nuisance alarms in process control
- 2003
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In: Notes SAIS/SSLS Joint Workshop.
-
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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| 3. |
- Ahnlund, Jonas, et al.
(author)
-
Rule-based reduction of alarm signals in industrial control
- 2003
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In: Journal of Intelligent & Fuzzy Systems. - 1064-1246. ; 14:2, s. 73-84
-
Journal article (peer-reviewed)abstract
- The proper handling of alarms is crucial to any automated process control. In practice, many alarms are only distractive and do not represent a potentially dangerous situation. This paper presents a methodology and a computerized tool that aims to remove such nuisance alarms, a so-called alarm cleanup. This is a general, systematic approach that takes advantage of the control system's built-in functions, and is a first step to an improved overall alarm situation. By the strong reduction of the alarm count, the efficient construction of fault diagnosis and isolation models becomes feasible. In a typical case study, the number of alarms received at the remote control room of an operational bio-fueled District Heating Plant was effectively reduced by 83%.
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| 4. |
- Bergquist, Tord, et al.
(author)
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Alarm reduction in industrial process control
- 2003
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In: EFTA 2003. 2003 IEEE Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.03TH8696). - 0780379373 ; 2, s. 58-65
-
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 is proposed that aims to reduce the occurrence of the so called nuisance alarms. It is a general computerized tool that takes advantage of the control system's built-in functions, and is a first step to an enhanced overall alarm situation. In the first prototype, a significant reduction of the alarms in a biofueled district heating plant has been achieved
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| 5. |
- 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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| 6. |
- Bergquist, Tord, et al.
(author)
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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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| 7. |
- Larsson, Jan Eric, et al.
(author)
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Improving expressional power and validation for multilevel flow models
- 2004
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In: Journal of Intelligent & Fuzzy Systems. - 1064-1246. ; 15:1, s. 61-73
-
Journal article (peer-reviewed)abstract
- Multilevel flow modeling (MFM) is a modeling method for complex technical systems in which the goals and functions of the system are explicitly described. MFM can be used as a basis for root cause analysis, where primary root causes are separated from consequential faults, in complex fault situations. Model representations for use in diagnostic reasoning usually describe causality, between parameters, faults, of, process states. However, the causality of a system may vary depending on details in the construction, as well as over time with the process state. One contribution of this paper is a general method of describing varying causality in a simple and efficient way. The method has been tested using multilevel flow models. Causality is visible in measurements and can be used to increase process understanding. The standard cross-correlation technique is insufficient for causality detection in industrial processes. Another contribution of this paper is a new method that can detect causality in industrial signals, and thus be used to validate the design of multilevel flow models.
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