Abstract
This paper presents a hybrid knowledge-based approach for leak monitoring in pipes and pipelines used for the transport of fluids (liquids, LPG, gases). The hybrid leak detection and localization scheme combines Real Time Transient Model (RTTM) based methods with Artificial Neural Networks (ANN). This enables to combine analytical knowledge available prior to system operation with knowledge by example, presented In form of measured process signals. Knowledge by example can be presented before and during system operation, enabling a flexible information processing scheme. A real application for a pipeline transporting petrochemical liquids demonstrates the performance of the approach.
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