Decimated Signal Diagonalization Method for Improved Spectral Leak Detection in Pipelines

Leak detection is an important issue in piping that deals with the management of water resources; nowadays large amounts of water in the network are dispersed as reported in current scientific literature. Among the methods for leak detection in water pipes, spectral analysis is very interesting. A classical spectral method is fast Fourier transform, but in this paper, we present an alternative method of spectral analysis, which has higher performance in terms of resolution and fast processing, namely decimated signal diagonalization (DSD). It is a nonlinear, parametric method for fitting time domain signals represented in terms of exponentially damped time signals. The aim is to reconstruct the unknown components as the harmonic variables, estimating the fundamental complex frequencies, and amplitudes. The DSD method partly uses the principles of the filter diagonalization method (FDM), which constructs matrices of a generalized eigenvalue problem directly from measured time signals of arbitrary length. However, the DSD because of its windowing technique produces a considerable reduction of size of the original data matrix, and consequently acquisition time can be shorter. We have tested the DSD method for leak detection problem in an experimental zigzag pipeline. We show as the DSD method produces good results in terms of resolution than FDM one