Automatic monitoring system for singularities detection in dikes by DTS data measurement
Résumé
The development of automated monitoring systems for detection of singularities such as leakages in dikes is indispensable to avoid mass disaster. An efficient solution for dikes survey is the use of Distributed Temperature Sensors (DTS) based on optical fiber, offering multitude of advantages such as low-cost, extreme robustness, long range measurement, etc. However, the temperature data acquired with DTS, being not directly interpretable, require intervention of signal processing techniques. The current work addresses this signal processing aspect, exploiting the key idea that the temperature variations over the course of a day for singular zones are quite different from those for nonsingular zones. A daily reference temperature variation, representative of the nonsingular zones, is estimated using the Singular Value Decomposition (SVD). The residue subspace of SVD contains information linked to the deviations from this reference; thus allowing the degree of singularity to be quantified by a dissimilarity measure such as the L2 norm. In order to detect only the singularities in dikes, such as leakages or drains, a Constant False Alarm Rate (CFAR) detector is proposed by modeling each daily dissimilarity measure with a mixture of Gamma and Uniform distributions. The proposed automatic singularity detection system was validated under different scenarios on real data over periods from 2005 to 2007. The first scenario depicted the detection of percolation type artificial leakages with their detection strength depending on their flow rates. Another scenario allowed to detect the presence of a real water leakage at the site, previously unobserved during manual inspections. The repeatability of the system was also verified by periodic analysis.
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