Alert Systems for production Plants : A Methodology Based on Conflict Analysis
Publikation: Forskning - peer review › Konferenceartikel i proceeding
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Alert Systems for production Plants : A Methodology Based on Conflict Analysis. / Nielsen, Thomas Dyhre; Jensen, Finn Verner.
Proceedings of the Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. IEEE Computer Society Press, 2005. s. 76-87 (Lecture Notes in Computer Science; Nr. 3571).Publikation: Forskning - peer review › Konferenceartikel i proceeding
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TY - GEN
T1 - Alert Systems for production Plants
T2 - Proceedings of the Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A1 - Nielsen,Thomas Dyhre
A1 - Jensen,Finn Verner
AU - Nielsen,Thomas Dyhre
AU - Jensen,Finn Verner
PB - IEEE Computer Society Press
PY - 2005
Y1 - 2005
N2 - We present a new methodology for detecting faults and abnormal behavior in production plants. The methodology stems from a joint project with a Danish energy consortium. During the course of the project we encountered several problems that we believe are common for projects of this type. Most notably, there was a lack of both knowledge and data concerning possible faults, and it therefore turned out to be infeasible to learn/construct a standard classification model for doing fault detection. As an alternative we propose a method for doing on-line fault detection using only a model of normal system operation, i.e., it does not rely on information about the possible faults. We illustrate the proposed method using real-world data from a coal driven power plant as well as simulated data from an oil production facility.
AB - We present a new methodology for detecting faults and abnormal behavior in production plants. The methodology stems from a joint project with a Danish energy consortium. During the course of the project we encountered several problems that we believe are common for projects of this type. Most notably, there was a lack of both knowledge and data concerning possible faults, and it therefore turned out to be infeasible to learn/construct a standard classification model for doing fault detection. As an alternative we propose a method for doing on-line fault detection using only a model of normal system operation, i.e., it does not rely on information about the possible faults. We illustrate the proposed method using real-world data from a coal driven power plant as well as simulated data from an oil production facility.
SN - 3540273263
BT - Proceedings of the Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
T3 - Lecture Notes in Computer Science
T3 - en_GB
SP - 76
EP - 87
ER -