Multi-leak Diagnosis in Pipelines A Comparison of Approaches

Leaks on pipelines can cause strong economic losses and environmental problems if these are not detected on time. The problem of detecting leaks is even more complicated when the pipelines are too large, difficult to reach by maintenance personnel, and equipped with minimum instrumentation. A compar...

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Bibliographic Details
Published in:	2008 Seventh Mexican International Conference on Artificial Intelligence pp. 352 - 357
Main Authors:	Verde, C., Morales-Menendez, R., Garza, L.E., Vargas, A., Velasquez-Roug, P., Rea, C., Aparicio, C.T., De la Fuente, J.O.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-10-2008
Subjects:	Artificial neural networks Environmental economics Environmental factors Fault Detection and Isolation Fault diagnosis Filtering Instruments Leak detection Leak Pipeline Personnel Pipelines Principal component analysis
Online Access:	Get full text
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Summary:	Leaks on pipelines can cause strong economic losses and environmental problems if these are not detected on time. The problem of detecting leaks is even more complicated when the pipelines are too large, difficult to reach by maintenance personnel, and equipped with minimum instrumentation. A comparison of four fault diagnosis approaches based on Output Observers, Artificial Neural Networks, Particle Filtering and Principal Components Analysis are presented. Simulated results of multi-leaks in pipelines showed that Particle Filtering techniques outperform the other approaches. However, a combined solution is proposed.
ISBN:	0769534414 9780769534411
DOI:	10.1109/MICAI.2008.33